Lung Injury Prediction Model in Bone Marrow Transplantation: A Multicenter Cohort Study
Svetlana Herasevich, Phillip J. Schulte, William J. Hogan, Hassan B. Alkhateeb, Zhenmei Zhang, Blaine C. White, Nandita Khera, Vivek Roy, Ognjen Gajic, Hemang Yadav
Abstract
Abstract Rationale Pulmonary complications contribute significantly to nonrelapse mortality following hematopoietic stem cell transplantation (HCT). Identifying patients at high risk can help enroll such patients into clinical studies to better understand, prevent, and treat posttransplantation respiratory failure syndromes. Objectives To develop and validate a prediction model to identify those at increased risk of acute respiratory failure after HCT. Methods Patients underwent HCT between January 1, 2019, and December 31, 2021, at one of three institutions. Those treated in Rochester, MN, formed the derivation cohort, and those treated in Scottsdale, AZ, or Jacksonville, FL, formed the validation cohort. The primary outcome was the development of acute respiratory distress syndrome (ARDS), with secondary outcomes including the need for invasive mechanical ventilation (IMV) and/or noninvasive ventilation (NIV). Predictors were based on prior case-control studies. Measurements and Main Results Of 2,450 patients undergoing stem cell transplantation, there were 1,718 hospitalizations (888 patients) in the training cohort and 1,005 hospitalizations (470 patients) in the test cohort. A 22-point model was developed, with 11 points from prehospital predictors and 11 points from posttransplantation or early (<24-h) in-hospital predictors. The model performed well in predicting ARDS (C-statistic, 0.905; 95% confidence interval [CI], 0.870–0.941) and the need for IMV and/or NIV (C-statistic, 0.863; 95% CI, 0.828–0.898). The test cohort differed markedly in demographic, medical, and hematologic characteristics. The model also performed well in this setting in predicting ARDS (C-statistic, 0.841; 95% CI, 0.782–0.900) and the need for IMV and/or NIV (C-statistic, 0.872; 95% CI, 0.831–0.914). Conclusions A novel prediction model incorporating data elements from the pretransplantation, posttransplantation, and early in-hospital domains can reliably predict the development of post-HCT acute respiratory failure.