Only three studies have previously predicted mortality 100-day post-discharge utilizing APACHE III [2, 4, 5]. To the best of our knowledge, this is the first study to evaluate the predictive value of PIC using the same formula after almost two decades utilizing updated APACHE predictions. It is noteworthy that the PPV of this PIC model utilizing APACHE III (93.3%) was sustained when applied to a more recent cohort utilizing APACHE IV (92.6%). These mortality predictions were in a replication population with a similar SMR (0.88) compared to the earlier cohort (0.91) .
For decisions regarding mortality prediction, a high specificity is required for use in family discussions regarding possible prognoses and outcomes. The high sensitivity of the predictive equation was maintained in the current study, in spite of the considerable time gap. Given the high specificity required for potential decision-making, a low sensitivity is expected. There was a significantly lower sensitivity in the updated database (from which we obtained the 2012‒2017 cohort) compared to the original database (wherein we obtained the 1995‒1997 cohort). The low sensitivity partially explains the smaller number of patients identified in any one-year period.
Since the PIC model was developed , many clinical features potentially influencing mortality risk and end-of-life management have undergone change, including (a) continued modifications in ICU design and process, with a focus on shared decision-making; (b) the progressive aging of the general population; (c) the changing of the cultural milieu regarding the process of dying (7); and physicians’ estimate of intensive care benefits [8–10].
In 2006, APACHE III was updated to APACHE IV. This update improved its accuracy and included patients with a history of coronary artery reconstruction [6, 7]. APACHE IV exhibited excellent discrimination and hospital mortality prediction that was statistically identical to that observed for the validation data set (13.55%). Although it is expected that the updated prediction equation would maintain sensitivity, it is possible that the difference in sensitivity between the original and updated PIC prediction equation is related to the use of APACHE IV. The sensitivity in the current cohort is consistent with an earlier study  that used APACHE III APS3 to predict PIC, which demonstrated a sensitivity of 0.15, similar to that in our study (0.11). The lack of a formal palliative care service 18 years ago might explain the reduced sensitivity in the current data set. Nevertheless, we have demonstrated that by utilizing both APACHE IV APM1 and APM5 data, 100-day mortality can be predicted with a high PPV over a prolonged period at a single institution utilizing the same formulas, which had been derived from APACHE III on a cohort two decades earlier.
Widely employed prognostic models for assessing overall severity of illness in critically ill adults include APACHE, the Simplified Acute Physiology Score (SAPS), and the Mortality Probability Model (MPMoIII). In a study with over 2,500 patients in three ICUs, Keegen et al. observed that APACHE III [0.868 (0.854‒0.880)] and IV [0.861 (0.847‒0.874)] exhibited statistically equivalent discrimination (area under the receiver operating characteristic curve with 95% confidence interval), which was significantly greater than that of the third iteration of SAPS [0.801 (0.785‒0.816)], which itself was better than that of the MPMoIII [0.721 (0.704‒0.738)] .
While our analysis represents mortality over a 100-day period, six-month morbidity and mortality have been analyzed for ICU patients receiving life-sustaining therapy. Factors that were associated with not returning to baseline included a higher APACHE III score and being non-white, elderly, recently hospitalized, with a history of transplantation, cancer, or neurologic or liver disease . Some authors have also researched the frequency and cost of treatment perceived to be futile, but the point at which treatment is deemed futile has varied . We utilized a 100-day mortality point, which could provide families with a concrete reference point.
Empirically based models estimating PIC in critically ill patients remain scarce. Furthermore, there has been a trend to embrace palliative care services. Clinical trends encouraging collaborative ICU provider-patient/surrogate decisions [14–16] motivated the present study to compare the reproducibility of our PIC model nearly 20 years after its original development. If these results can be duplicated at other institutions, they could have broad implications. Proactive identification of candidates for palliative care may be warranted when organ dysfunction does not respond to treatment and intensive care goals cannot be realized, or when life support becomes disproportional to the expected prognosis.
Integrating palliative with intensive care allows preferences of patients and families to be transparent, while also fostering dignity . This integration of palliative care with ICU treatment also reduces the length and costs of hospital stay . Patients who receive palliative care consultations are more likely to have a code status change, be transferred to hospice, and be treated with comfort measures [17, 18]. Multiple studies have encouraged collaborative ICU provider-patient/surrogate decisions [13–16]. Since the PPV has statistical precision representing the “prospective” discriminant utility of PIC modeling, it could be used to complement the contextual, continuous, and evolving process of clinical decision-making . This model may not have been pursued in the past due to the emphasis on autonomy. However, newer clinical approaches to the concept of autonomy suggest data from this model could be utilized in collaborative discussions [19, 20].
In this study, our PIC model had a significantly lower sensitivity, compared with the original study. The current study also had the limitation of being a single institution study.