Our predictive model could estimate the remaining time to death within 120 minutes before cardiac arrest under the palliative care situation with a valid prognostic ability. In detail, our predictive model showed 52.7% sensitivity, 79.8% specificity, 35.7% positive predictive value, and 88.8% negative predictive value. The model accuracy was superior to the predictive models in the previous studies: 42% sensitivity and 76% specificity by Lewis, et al. [4]; 39% sensitivity and 96% specificity by Coleman, et al. [5]; and 79% sensitivity and 63% specificity by DeVita, et al [13]. The novelty in this study is that we used continuously measured vital signs which were record every one minute in order to develop our predictive model. Brieva, et al. reported that the evaluation by intensivists is the clinical standard to predict time to death after withdrawal of cardiorespiratory support, and the evaluation was done at an arbitrary timing [14]. In this study, we repeatedly predicted the time of cardiac arrest by taking advantage of data from physiological monitor, and this continuity is one of other advantages against previous studies. In the past researches, different parameters have been reported to predict cardiac arrest, which include not only physiological data but also a blood test, a past medical history, and a background [15-17]. Our predictive model is a computer-based, and therefore, automatically calculated by using SBP and heart rate, which makes our model easily adoptable in any existing clinical workflows. Furthermore, although we developed this model in the ICU setting, the necessary parameters are commonly used standard variables so that it can be applied not only to the critical care settings but also chronic care settings.
Another key finding is the association between the breakdown of the autonomic nervous system and the time to death. Several studies reported the analysis of HRV, which was determined by the interaction between the sympathetic and parasympathetic autonomic nervous system, is a useful predictor of death [6-8]. In our predictive model, systolic blood pressure and heart rate were used as predictive parameters which represented this interaction. However, HRV should be carefully interpreted, and the control of breathing is crucial for accurate interpretation [18]. Billman found that HRV was associated with mechanical events due to the change in thoracic pressure and cardiac filling pressure during respiration [18]. Furthermore, Chiang, et al. reported that cardiac arrhythmia and major cardiac surgery affect HRV [7]. In this study, indeed, we focused on the balance between blood pressure and heart rate, not on HRV though we collected the data. In a controlled cardiac nervous system, blood pressure and heart rate are linked by the sympathetic and parasympathetic nervous system [19]. In the future study, we will develop a predictive model by targeting individuals and determining an equation based on the pattern of autonomic nervous system per patient. Our unique continuous measurement of vital signs can repeatedly predict the imbalance between the sympathetic and parasympathetic nervous systems, making the model higher prognostic ability.
In the palliative care setting, it is of significant importance to take enough time for patients to stay with their family members before postmortem changes occur. Predicting the time to death 120 minutes before cardiac arrest occurs allows most family members to reach the patient’s bed before the patient dying, especially in the urban setting. Also, prognostication of death may also help healthcare professionals to plan for a better patient care and communicate earlier with family members.
As mentioned, the accurate prognostication of the remaining time to cardiac arrest would be valuable for healthcare professionals in that they can allow the patient’s family members to say their final goodbyes to the patient who is imminently dying. However, healthcare professionals should always discuss with the family carefully whether or not they want to know how much time remains before cardiac arrest. Individuals have different attitudes toward death and may be influenced by religious beliefs. Therefore, in clinical practice, it is important to recognize that formulas may interfere with the dying process.
Limitations
There are several limitations to this research. Firstly, the sample size was small with 19 subjects, even though a large number of vitals were recorded per patient. The reason for this sample size is that in our hospital patients diagnosed as terminal, which was defined as being unresponsive to treatment for primary disease and having hemodynamic instability, were transferred to a different hospital ward to allow them to spend the last time with their family members. Therefore, in general, very few patients go into cardiac arrest while staying in the ICU. Secondly, although we conducted statistical analyses by using repeatedly measured vitals from different patients as one cluster, they might include bias due that an individual dataset had many data collected from the same patient. To solve them, a future study should increase sample size of patients. Thirdly, although we defined 120 minutes as the threshold for allocating data to the two groups of VS and TS, the predictive model for 120 minutes before cardiac arrest may not be enough for a patient family to reach the hospital, especially in the rural setting. It might be, indeed, more appropriate to use a different timeframe to differentiate these two groups. Lastly, our predictive model required continuous invasive blood pressure and heart rate monitoring. Besides the ICU settings, non-invasive blood pressure monitoring is typically used to measure patient’s blood pressure so that future studies targeting non-ICU patients should select alternative variables, measurement interval, and monitoring devices [19]. This new predictive model that estimates the remaining time to cardiac arrest had a reasonable prognostic ability. In the palliative and EOL care settings, this model can predictably identify the malfunction of the autonomic nervous system by continuously tracking the variations in heart rate and systolic blood pressure before cardiac arrest. Further research will be required to examine individual patterns of fluctuations in hemodynamic parameters in the palliative care situation prior to death. Furthermore, it is important to develop a predictive model that is implemented in noninvasive ways and can prognosticate the remaining time to cardiac arrest in different clinical settings.