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PURPOSE This study evaluated risk factors predicting unplanned 30-day acute service utilization among adults subsequent to hospitalization for a new diagnosis of leukemia, lymphoma, or myeloma. This study explored the prevalence of medical complications (aligned with OP-35 measure specifications from the Centers for Medicare & Medicaid Services [CMS] Hospital Outpatient Quality Reporting Program) and the potential impact of psychosocial factors on unplanned acute care utilization. METHODS This study included 933 unique patients admitted to three acute care inpatient facilities within a nonprofit community-based health care system in southern California from 2012 to 2017. Integrated comprehensive data elements from electronic medical records and facility oncology registries were leveraged for univariate statistics, predictive models constructed using multivariable logistic regression, and further exploratory data mining, with predictive accuracy of the models measured with c-statistics. RESULTS The mean age of study participants was 65 years, and 55.1% were male. Specific diagnoses were lymphoma (48.7%), leukemia (35.2%), myeloma (14.0%), and mixed types (2.1%). Approximately one fifth of patients received unplanned acute care services within 30 days postdischarge, and over half of these patients presented with one or more symptoms associated with the CMS medical complication measure. The predictive models, with c-statistics ranging from 0.7 and above for each type of hematologic malignancy, indicated good predictive qualities with the impact of psychosocial functioning on the use of acute care services ( P values < .05), including lack of consult for social work during initial admission (lymphoma or myeloma), history of counseling or use of psychotropic medications (lymphoma), and past substance use (myeloma). CONCLUSION This study provides insights into patient-related factors that may inform a proactive approach to improve health outcomes, such as enhanced care transition, monitoring, and support interventions.

312 Background: Improving the ability to predict which patients are at increased risk for acute care services subsequent to hospitalization can lead to more effective interventions and quality care. This study evaluated the performance of risk models to predict 30-day acute service utilization among adult inpatients with newly diagnosed hematologic malignancies. The study explored the impact of medical complications on potentially preventable service utilization, aligned with specifications for proposed measure OP-35 from the CMS Hospital Outpatient Quality Reporting Program. Methods: The study included 933 unique adult patients admitted to four acute care inpatient facilities within a non-profit community based healthcare system during calendar years 2012 to 2017. Risk models integrated a comprehensive set of data elements using clinical information from electronic medical records and facility oncology registries. Predictive models were constructed using a multivariable logistic regression, with calculation of standardized coefficients to rank order variables with the greatest impact on unplanned readmissions. Exploratory data mining techniques were conducted to augment classification of at-risk patients. Results: The mean age of study participants was 65 years and 55.1% were male. In order of prevalence, the types of hematologic malignancy were lymphoma (48.7%), leukemia (35.2%), myeloma (14.0%), and mixed types (2.1%). Approximately one fifth of patients received unplanned acute care services within 30 days after the index hospitalization (lymphoma = 23.1%, myeloma = 22.1%, leukemia = 18.6%). Among the risk models, the most influential predictors included initial length of stay, severity of illness, and consult for social work. Overall, 56.6% of patients who were readmitted within 30 days of hospitalization presented with one or more symptoms associated with the CMS medical complication measure, including cytopenia, infection, negative hydration, and/or pain. Conclusions: Optimizing quality care in a value-based health care environment requires proactive approaches such as risk prediction models for identifying patients who can benefit most from care transition interventions.