Building a Clinically Relevant Risk Model: Predicting Risk of a Potentially Preventable Acute Care Visit for Patients Starting Antineoplastic Treatment
| Autor: | Wendy Perchick, Kevin Nicholas, Lauren L. Katzen, Claire Perry, Stefania Sokolowski, Dmitriy Gorenshteyn, Isaac Wagner, Alice Zervoudakis, Diane Reidy-Lagunes, Lynn Adams, Rori Salvaggio, Margarita Rozenshteyn, Han Xiao, Lior Gazit, Brett A Simon, Yeneat O. Chiu, Bobby Daly, Abigail Baldwin Medsker |
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| Rok vydání: | 2020 |
| Předmět: |
Male
medicine.medical_specialty MEDLINE Risk Assessment 03 medical and health sciences Risk model 0302 clinical medicine Risk Factors Acute care Neoplasms Original Reports medicine Electronic Health Records Humans 030212 general & internal medicine Medical Informatics Applications Intensive care medicine Aged Models Statistical business.industry Extramural General Medicine Middle Aged Hospitalization 030220 oncology & carcinogenesis Female business Emergency Service Hospital |
| Zdroj: | JCO Clin Cancer Inform |
| ISSN: | 2473-4276 |
| Popis: | PURPOSE To create a risk prediction model that identifies patients at high risk for a potentially preventable acute care visit (PPACV). PATIENTS AND METHODS We developed a risk model that used electronic medical record data from initial visit to first antineoplastic administration for new patients at Memorial Sloan Kettering Cancer Center from January 2014 to September 2018. The final time-weighted least absolute shrinkage and selection operator model was chosen on the basis of clinical and statistical significance. The model was refined to predict risk on the basis of 270 clinically relevant data features spanning sociodemographics, malignancy and treatment characteristics, laboratory results, medical and social history, medications, and prior acute care encounters. The binary dependent variable was occurrence of a PPACV within the first 6 months of treatment. There were 8,067 observations for new-start antineoplastic therapy in our training set, 1,211 in the validation set, and 1,294 in the testing set. RESULTS A total of 3,727 patients experienced a PPACV within 6 months of treatment start. Specific features that determined risk were surfaced in a web application, riskExplorer, to enable clinician review of patient-specific risk. The positive predictive value of a PPACV among patients in the top quartile of model risk was 42%. This quartile accounted for 35% of patients with PPACVs and 51% of potentially preventable inpatient bed days. The model C-statistic was 0.65. CONCLUSION Our clinically relevant model identified the patients responsible for 35% of PPACVs and more than half of the inpatient beds used by the cohort. Additional research is needed to determine whether targeting these high-risk patients with symptom management interventions could improve care delivery by reducing PPACVs. |
| Databáze: | OpenAIRE |
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