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- MATERIALS AND METHODS
- FUNDING SOURCES
The majority of cancer patients, when asked about end-of-life care preferences, prefer to die at home rather than in a hospital. In spite of this, approximately 30% of these patients die in the hospital, and 9% receive an intensive care unit level of care. Cancer patients who die in the hospital may experience a substantially greater physical and psychological burden. At the same time, families report significant distress associated with in-hospital death and an increased risk of developing posttraumatic stress and prolonged grief.[10, 11] Advance care planning is often limited to care at the close of life with discussion of hospice, location of death, advance directives, and health care surrogacy only being done when there are no further anticancer treatments available, patients are hospitalized, and/or patients are developing refractory, difficult-to-control symptoms.[12, 13] Improvement in end-of-life care and avoidance of undesired hospital deaths in patients who have advanced cancer might be achieved if patients with an increased risk of short-term mortality could be identified.
Physicians' ability to prognosticate is also flawed. On average, physicians tend to overestimate life expectancy by a factor of 3. Although significant clinical symptoms and poor performance status can portend a poor prognosis (eg, dysphagia, weight loss); prediction of survival becomes less accurate and more difficult in patients with a good performance status.[18, 19]
Although there are prognostic instruments to help clinicians, the tools currently available have significant shortcomings. These include difficulty in clinical application and lack of validity in the inpatient hospital setting. For example, validated instruments including the Palliative Prognostic Score (PaP), the Palliative Prognostic Index (PPI), and more recently the Japan Palliative Oncology Study Prognostic Index, and Prognosis in Palliative Care Study require face-to-face clinical assessment to determine key prognostic factors including performance status, extent of disease and symptom burden. The PaP, and Japan Palliative Oncology Study Prognostic Index each necessitates a clinical prediction of survival which, as pointed out above, has inherent flaws. The external validity of these measures may also be questionable, because most were not derived or rigorously applied in an inpatient hospital setting with a diverse population of patients.[25, 26] In fact, most of these studies were done on patients who were already determined to be terminally ill, making these tools less relevant for patients who are still receiving anticancer therapy.[22, 27] Tools such as the PaP and PPI were also developed at a time when the electronic medical record (EMR) could not be appropriately leveraged to aid in clinical decision-making. Chiang et al attempted to overcome this limitation using computer-assisted technology, but still focused on cancer patients who were already determined to be terminally ill and/or enrolled in hospice. Finally, even the most vetted prognostic tools available do not meet the most rigorous criteria of high discriminatory ability (C statistic > 0.90), independent validation, and transportability.
Given the disconnect between the expressed wishes of patients regarding where they wish to die and what actually happens, the lack of early discussion around advance care planning, along with the shortcomings of available prognostic measures, we sought to develop a prognostic tool that predicts 30-day mortality among hospitalized cancer patients, including patients still receiving active cancer therapy. The tool was constructed using data only from the EMR with the goal that every cancer patient can be screened within 24 hours of hospitalization.[32, 33] The rapid identification of patients with increased 30-day mortality allows health care providers to address the goals of care of these patients, identify patient and family needs, and provide care that is consistent with these goals.
- Top of page
- MATERIALS AND METHODS
- FUNDING SOURCES
In this study, we were able to identify clinical and administrative factors readily obtainable through the EMR that were associated with an increased risk of 30-day mortality among cancer patients admitted to a hematology/oncology or bone marrow transplant service. Clinical factors at time of hospital admission associated with an increased risk of death included older age and impairment in functional status. Variables obtained on hospital admission that were significant included vital sign abnormalities (temperature, heart rate, systolic blood pressure, pulse oximetry), and oxygen use. Finally, admission type (elective versus emergent) was also associated with 30-day mortality. Our findings support the feasibility of using the EMR data to identify cancer patients on day 1 of admission who are at a higher risk of 30-day mortality.
Similar to prior studies, we found that certain clinical factors such as performance status, oxygen use, and patient age are associated with a higher risk of patient death.[21, 22, 35] Interestingly, some laboratory parameters that would be expected to be associated with mortality, such as WBC count, were not significant in the multivariate model.[21, 38] We differed from prior studies in that we excluded factors that we believed would require face-to-face clinical assessment, such as extent of disease, intake, clinical signs (ie, ascites) or symptoms, and clinical prediction of survival.[20, 28, 37, 39, 40] These factors would limit the utility of this tool as an automated screen.
Two key factors distinguish our study from prior research in this area: 1) a focus on hospitalized cancer patients who have not yet progressed to intensive care, and 2) use of the EMR exclusively to glean data for the predictive model.
There is a dearth of prognostication models developed for the inpatient setting prior to admission into intensive care. Most of the well-known models that have been validated for patients with cancer were initially developed in the home-hospice or palliative care setting. This includes well-known prognostic tools such as the PaP, the PPI, as well as the Palliative Performance Scale.[21, 22, 28, 39, 40] Consequently, these scoring systems are better predictors in patients with a shorter prognosis, and are less well-suited for patients with the potential for a longer prognosis. A number of prognostic scoring systems/prediction models, such as the Mortality Prediction Model, Acute Physiology and Chronic Health Evaluation (APACHE) II, or the Simplified Acute Physiology Score (SAPS) II,[38, 41] are used in cancer patients who are already admitted into intensive care units Although these models are useful for the medical team, they are of limited value to patients and families. Patients who have progressed to intensive care are frequently unable to contribute substantively to a discussion of their goals and values. Thus, identifying patients who are at risk of mortality in the near term while they are still able to clearly communicate their end-of-life wishes is imperative.
Amarasingham et al developed a real-time electronic predictive model based on hospital admission data to predict 30-day mortality or readmission for patients with heart failure. This was also done by Tierney et al in an outpatient cardiac patient population. Our model is the first of its type we are aware of for cancer patients. Similar to Amarasingham et al, we show that a simple equation using baseline admission data can identify cancer patients at higher risk for 30-day mortality. This type of model would facilitate large-scale screening of cancer patients on day 1 of admission and allow physicians to identify patients who may benefit from further assessment of their prognosis via clinical evaluation. Patients who screen positive and are determined to have a short prognosis based on clinical judgment would benefit from further goals of care conversations and advance care planning.[17, 19]
Our study has several limitations. The study was conducted at a single tertiary care institution, which limits generalizability to other hospitals and settings. Our data relies on the EMR with inherent variability in accuracy, availability of data (eg, missing data), and standard assessments that may not conform with our clinical assessments (ie, nursing determination of functional status via ADLs versus ECOG or Karnofsky performance status). Our assumption would be that as the EMR evolves and becomes more clinically salient, tools such as these can also become more accurate and relevant. Our findings are based on a retrospective cohort analysis, and future studies need to prospectively validate these results. Also, the clinical utility of this tool that includes patient, caregiver, and system-related outcomes needs to be established. In an effort to address some of these limitations, particularly the exclusion of potentially important variables due to missing data, we plan to refine this instrument in future analyses that use larger and more complete data sets.
In this study, we establish the feasibility of using the EMR to identify cancer patients on hospital admission who are at increased risk for 30-day mortality by fitting the model on a training sample and validating it on an independent test sample. This approach has the potential to rapidly and continuously screen all cancer patients admitted to the medical/surgical units. This is particularly important near the end of life, when patient, family, and medical team discussions are needed to address the preferred location of death, goals of symptom management, use of hospice, and hospital readmission. The application of such an approach can help clinicians readily identify patients with a high risk of short-term mortality to ensure prognosis is integral to development of the care plan. This will avoid potentially harmful interventions, and ensure incorporation of patient goals and values into the patient's plan of care.