Differences Between Low-Risk Survivors and Nonsurvivors
There were no differences between both groups in age and gender (Table 2). Although the predicted mortality risk was low, there was a small but statistically significant difference in predicted risk between nonsurvivors and survivors in both prediction models.
Nonsurvivors compared to survivors had more unplanned admissions (74.4% vs 38.5%; p < 0.001), CCCs (76.7% vs 58.8%; p = 0.001) and were more often mechanically ventilated (88.1% vs 34.9%; p < 0.001). Nonsurvivors were more often admitted for circulatory problems, more often admitted outside office hours, and more often transported with a specialized transport upon admission (Table 2). Furthermore, nonsurvivors had significantly more ventilator days (median, 9 [IQR, 3–22] vs median, 2 [IQR, 1–3]; p < 0.001) and longer length of stay (median, 11 [IQR, 5–32] d vs median, 3 [IQR, 2–5] d; p < 0.001) compared to survivors.
Factors Associated With Mortality in Low-Risk Patients
Based on the univariable analysis, review of the literature and expert opinion, the following seven variables age, admission outside office hours, CCCs, unplanned admissions, readmissions, specialized transport, and season of admission were considered as relevant risk factors and were subsequently included in multivariable logistic regression analysis. This showed that CCCs and unplanned admissions were most significantly associated with mortality (odds ratio [OR], 3.29 [95% CI, 1.97–5.50] and OR, 5.78 [95% CI, 3.40–9.81], respectively) (Table 3). Furthermore, whether a patient was admitted between April and September was associated with increased mortality (OR, 1.67 [95% CI, 1.08–2.58]).
In this large cohort study of PICU patients with a low predicted mortality risk in The Netherlands, several differences were found between survivors and nonsurvivors. CCCs and unplanned admissions were more prevalent among nonsurvivors when compared with survivors, and in addition, they were strongly associated with mortality.
The hospitalization rates of children with multiple CCCs have been increasing over the last decades (20). The association between CCCs and mortality in our study is in accordance with the current literature. The association of CCCs with PICU mortality was established in the overall PICU population admitted to 54 units in the United States (16). This study shows that CCCs are common not only in the PICU population in general but also in patients with low severity of illness as scored by PRISM and PIM2. Although some CCCs are incorporated in the PIM2, neither the PIM2 nor PRISM model scores CCCs completely. The declining mortality rate in PICUs combined with the increasing prevalence of patients with CCCs suggests that PICU outcome studies should shift their focus from mortality to morbidity (21).
Also, an association was found between unplanned admissions and mortality. It is likely that unplanned low-risk admissions form a different, more seriously ill, group than planned low-risk admissions, despite relative normal physiology and laboratory results at admission. No association was found between off-hours admissions and mortality. Other studies on subject in adult and PICUs show inconsistent results (18, 22–27). Results of these studies might be influenced by structural factors like nursing and medical staffing during off-hours. No association was found between PICU readmissions within 48 hours and mortality, which is in contrast with a North American study (28). This might be due to the low number of readmissions in the low-risk survivor and the nonsurvivor group in our study. The increased OR for death associated with admission between April and September compared with winter months is counterintuitive (26, 29–31). We can only speculate on this.
One of the main differences between survivors and nonsurvivors is the larger number of ventilator days and increased length of PICU stay in nonsurvivors. It appears that most of the low-risk nonsurvivors probably deteriorated after admission and after the recording of the values used to calculate the mortality prediction scores, resulting in residual confounding. This is in accordance with the literature on this subject showing a decrease of the predictive capability of the models in patients with a longer length of stay and in patients with a higher predicted mortality risk and a long length of stay (9, 32). The increased length of PICU stay is also associated with the number of adverse events (33, 34). This could possibly have influenced the length of stay and outcome in this group.
Our study has several limitations. First, we considered an admission as low risk when either the PRISM- or the PIM2-predicted risk of mortality was less than 1%. This choice was arbitrary, since there is no consensus about a cut-off point for low risk of mortality (4). On the other hand, both the PICE and ANZPIC report a risk of less than or equal to 1% as the lowest level of mortality risk in their tables meaning this cut-off point is generally accepted in the field. Second, the PIM2 and PRISM prediction models are not intended for individual patients, but for groups of patients and for individual patients these models misrepresent their actual risk of mortality. For example, patients with congenital heart disorders sometimes are admitted preoperatively to the PICU, prior to their critical issues and not detected by risk scores. New PRISM methods deal with this issue and might reflect mortality risk better in this type of patients in the future (5). Similarly, there is often a difference between the two scores, which in the majority of our cases resulted in a patient being considered low risk by one prediction model and not by the other. To a certain degree, a difference between scores is expected: the prediction models include data on different predictors and in different time windows. We only found gradual differences between the two models. The aim of the study was not to choose the best model for predicting the low-risk patient but to determine factors influencing outcome in this population.
Finally, nonsurvival was defined based on PICU mortality and not on long-term follow-up, possibly introducing bias. Data on hospital mortality or long-term follow-up unfortunately were not available in the registry. However, the prediction models are based on PICU mortality as well and not on hospital mortality.
Our results indicate that future research is necessary to determine whether adding complex chronic conditions to the currently used mortality prediction models would result in improvement of the performance of these models, especially for the low-risk category PICU patients. It seems that there are more factors involved in nonsurvival in the low-risk PICU population. Besides deteriorations of patients’ condition over time and socioeconomic status that has been linked to health and mortality (35), it would be of interest to determine adverse events emerging during a prolonged PICU stay. Studies in adult ICU patients show that adverse events are associated with increased mortality (36–38). A retrospective study showed a substantial amount of preventable deaths in hospitals in The Netherlands (39), but prevalence has not been determined in the low-risk PICU population. From the perspective of quality improvement, limiting adverse events could be a modifiable factor in the death of these patients.
Although the number of low-risk admissions is high in the PICU, the total number of nonsurvivors is—as expected by risk models—low. The absolute number of nonsurvivors in the high-risk population is much higher. It would be interesting to investigate that whether in the high-risk population the same or other risk factors influence mortality.
Children dying in the PICU with a low predicted mortality have recognizable risk factors including complex chronic conditions and/or emergency admissions.
Members of SKIC (Dutch collaborative PICU Research Network) were as follows: Dick van Waardenburg, MD, PhD (Department of Pediatric Intensive Care, Academic Hospital Maastricht, Maastricht, The Netherlands); Douwe van der Heide, RN (Faculty Board Member, PICE Registry, The Netherlands); Nicolette A. van Dam, MD (Department of Pediatric Intensive Care, Leiden University Medical Center, Leiden, The Netherlands); Nicolaas J. Jansen, MD, PhD (Department of Pediatric Intensive Care, University Medical Center Utrecht, Utrecht, The Netherlands); Mark van Heerde, MD, PhD (Department of Pediatric Intensive Care, VU University Medical Center, Amsterdam, The Netherlands); Cynthia van der Starre, MD, PhD (Department of Neonatal and Pediatric Intensive Care, Erasmus University Medical Center-Sophia Children’s Hospital, Rotterdam, The Netherlands); Roelie van Asperen, MD, PhD (Department of Pediatric Intensive Care, University Medical Center Utrecht, Utrecht, The Netherlands); Martin Kneyber, MD, PhD, FCCM (Department of Pediatric Intensive Care, University Medical Center Groningen, Groningen, The Netherlands); and Job B. van Woensel, MD, PhD (Department of Pediatric Intensive Care, Academic Medical Center, Amsterdam, The Netherlands).
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child; chronic complex condition(s); mortality; outcome assessment (healthcare); pediatric intensive care
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