Christian Peter Subbe's publications, visit PubMed. This is an unprecedented time. It is the dedication of healthcare workers that will lead us through this crisis. Thank you for everything you do. Calc Function Calcs that help predict probability of a disease Diagnosis. Subcategory of 'Diagnosis' designed to be very sensitive Rule Out. Disease is diagnosed: prognosticate to guide treatment Prognosis. Numerical inputs and outputs Formula. Med treatment and more Treatment.
Suggested protocols Algorithm. Disease Select Specialty Select Chief Complaint Select Organ System Select Log In. Email Address. Password Show. Or create a new account it's free. Chest ; 98 : — Franklin C, Mathew J.
Developing strategies to prevent inhospital cardiac arrest: analysing responses of physicians and nurses in the hours before the event. Crit Care Med ; 22 : —7. Unexpected deaths and referrals to intensive care of patients on general wards: are some potentially avoidable? J R Coll Physicians Lond ; 33 : —9.
Confidential inquiry into quality of care before admission to intensive care. Br Med J ; : —8. Anaesthesia ; 54 : — An Early Warning Scoring System for detecting developing critical illness. Clin Intens Care ; 8 : Prospective evaluation of a Modified Early Warning Score to aid earlier detection of patients developing critical illness on a general surgical ward. Crit Care Med ; 13 : — A method for predicting survival and mortality of ICU patients using objectively derived weights.
Crit Care Med ; 13 : —5. Predicting patient outcome from acute renal failure comparing three general severity of illness scoring systems. Kidney Int ; 58 : — Results of report cards for patients with congestive heart failure depend on the method used to adjust for severity. Ann Intern Med ; : 10 — A simplified acute physiology score for ICU patients.
Crit Care Med ; 12 : —7. JAMA ; : — Crit Care Med ; 17 : — Intensive Care Med ; 23 : — Assessment of prognosis of coronary patients: performance and customization of generic severity indexes. Chest ; : — Simplification of the SAPS by selecting independent variables. Intensive Care Med ; 17 : —8. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide.
Sign In or Create an Account. Sign In. Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents Abstract. Validation of a modified Early Warning Score in medical admissions. Subbe , C. Oxford Academic. Google Scholar. Revision received:. Two-sided P values of less than 0. A total of 11, MET activation events had occurred during the study period; the median annual number of activations was Table 1 shows the demographic and activation characteristics of the patients according to day mortality.
The physiologic parameters of the patients at the time of MET activation are presented in Table 2. The treatment outcomes are shown in Table 3. Age, comorbidities, medical department, days from admission, activation during daytime hours, activation by screening, activation causes, vital signs, mental status, and oxygen supply were identified as the predictors of day mortality.
Dashed lines showed the rates of ICU transfer within 24 hours after the activation and solid lines showed day mortality at each score. Shappell et al. Our results were similar in that the predictive power of the logistic regression model was significantly higher than that of NEWS. Fernando et al. In addition, difference in the severity of the patients may have resulted in such difference—whereas We identified multiple clinical characteristics as well as physiologic parameters at the time of MET activation that were significantly associated with day mortality.
Among them, the findings on vital signs, mental status, and oxygen supply are consistent with those of previous studies regarding EWS or MET [ 9 , 11 , 18 , 19 ]. Our result is in line with the results of Shappell et al. Some results are not in line with those from previous studies. In our patients, peripheral oxygen saturation was not associated with day mortality, which may be because it can be rapidly corrected with oxygen therapy by general ward physicians.
Several studies reported that older age at MET activation, especially 75 years or more, was associated with higher mortality [ 11 , 20 , 21 ].
In contrast, in the current study, patients aged 73 years or more had lower odds of day mortality than those aged 53 years or less. Also, MET activation during nighttime was associated with lower day mortality compared with daytime activation, which is in conflict with previous studies [ 21 , 22 ]. This may be attributable to the difference in the activation method of the MET: while the MET was activated only by calling from the ward staff in those studies, MET activation in our study was based on electronic medical record-based screening as well as calling.
Our results suggest that electronic medical record-based screening and subsequent intervention by the MET could compensate for relative insufficiency in the number and experience of general ward medical staffs during nighttime. The current study is distinct from previous studies investigating the prognostic accuracy of the EWS in that patients with do-not-resuscitate orders were excluded. An important aspect of MET activities is aiding in the decision on a do-not-resuscitate order and providing end-of-life care.
However, the decision on a do-not-resuscitate order is mostly based on medical futility and patient autonomy [ 23 ], not on the severity of the patient. Thus, if patients with do-not-resuscitate orders are included when evaluating the predictive power of EWS, the results may be inadequate for application in clinical settings in which an MET should determine the level of intervention based on the severity of the patient. Nevertheless, as the goal of this study was to investigate whether EWS at the time of activation was useful for the MET in assessing the severity and prognosis of patients, it was appropriate to use EWS at the time of MET activation.
At our center, the ACDU scale has been used to detect deterioration in mental status earlier. However, we converted the ACDU scale to the AVPU scale in the EWS based on previously reported data [ 14 ], and the higher adjusted odds ratio of the confused patients to the alert patients in our data also supports the validity of this conversion.
Thirdly, the study results may have limited generalizability because this study was conducted in a single center. Specifically, more than half of our patients To assess the outcome of the MET activation event itself rather than the outcome of underlying malignancy, we defined the primary outcome as day mortality from MET activation. Lastly, the original goal of the EWS is not to stratify the risk of death in patients with MET activation, but to herald acute deterioration and to trigger clinical responses accordingly in general in-patients.
However, if a MET can accurately stratify the severity or prognosis of the patient at activation, it will help to decide the level of intervention, treatment priority, and allocation of medical resources. This warrants the need for the development of a new, practical scoring system for use by the MET in deciding the optimal treatment of patients and the allocation of medical resources.
Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Background The current early warning scores may be insufficient for medical emergency teams METs to use in assessing the severity and the prognosis of activated patients. Methods Adult general ward inpatients who activated the MET in a tertiary referral teaching hospital between March and December were included. Results A total of 6, MET activation events were analyzed.
Introduction Patients in general wards can undergo acute deterioration, resulting in poor outcomes including unexpected intensive care unit ICU transfer, cardiac arrest, or death.
Materials and methods Study patients This retrospective cohort study was conducted at Asan Medical Center, a tertiary referral teaching hospital with approximately , adult inpatients per year. Data collection At the end of each MET activation, the MET nurse who participated in the activation event recorded the patient's data based on the medical record.
Statistical analysis Data are presented as either mean with standard deviation or median with interquartile range IQR. Results A total of 11, MET activation events had occurred during the study period; the median annual number of activations was
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