Browsing by Author "Zaribafzadeh, Hamed"

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    CPT to RVU conversion improves model performance in the prediction of surgical case length.
    (Scientific reports, 2021-07-08) Garside, Nicholas; Zaribafzadeh, Hamed; Henao, Ricardo; Chung, Royce; Buckland, Daniel
    Methods used to predict surgical case time often rely upon the current procedural terminology (CPT) code as a nominal variable to train machine-learned models, however this limits the ability of the model to incorporate new procedures and adds complexity as the number of unique procedures increases. The relative value unit (RVU, a consensus-derived billing indicator) can serve as a proxy for procedure workload and could replace the CPT code as a primary feature for models that predict surgical case length. Using 11,696 surgical cases from Duke University Health System electronic health records data, we compared boosted decision tree models that predict individual case length, changing the method by which the model coded procedure type; CPT, RVU, and CPT-RVU combined. Performance of each model was assessed by inference time, MAE, and RMSE compared to the actual case length on a test set. Models were compared to each other and to the manual scheduler method that currently exists. RMSE for the RVU model (60.8 min) was similar to the CPT model (61.9 min), both of which were lower than scheduler (90.2 min). 65.2% of our RVU model's predictions (compared to 43.2% from the current human scheduler method) fell within 20% of actual case time. Using RVUs reduced model prediction time by ninefold and reduced the number of training features from 485 to 44. Replacing pre-operative CPT codes with RVUs maintains model performance while decreasing overall model complexity in the prediction of surgical case length.
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    ItemOpen Access
    Development, Deployment, and Implementation of a Machine Learning Surgical Case Length Prediction Model and Prospective Evaluation.
    (Annals of surgery, 2023-06) Zaribafzadeh, Hamed; Webster, Wendy L; Vail, Christopher J; Daigle, Thomas; Kirk, Allan D; Allen, Peter J; Henao, Ricardo; Buckland, Daniel M

    Objective

    Implement a machine learning model using only the restricted data available at case creation time to predict surgical case length for multiple services at different locations.

    Background

    The Operating Room (OR) is one of the most expensive resources in a health system, estimated to cost $22-133 per minute and generate about 40% of the hospital revenue. Accurate prediction of surgical case length is necessary for efficient scheduling and cost-effective utilization of the OR and other resources.

    Methods

    We introduced a similarity cascade to capture the complexity of cases and surgeon influence on the case length and incorporated that into a gradient boosting machine learning model. The model loss function was customized to improve the balance between over- and under-prediction of the case length. A production pipeline was created to seamlessly deploy and implement the model across our institution.

    Results

    The prospective analysis showed that the model output was gradually adopted by the schedulers and outperformed the scheduler-predicted case length in Aug-Dec 2022. In 33,815 surgical cases across outpatient and inpatient platforms, the operational implementation predicted 11.2% fewer under-predicted cases and 5.9% more cases within 20% of the actual case length compared with the schedulers and only over-predicted 5.3% more. The model assisted schedulers to predict 3.4% more cases within 20% of the actual case length and 4.3% fewer under-predicted cases.

    Conclusions

    We created a unique framework that is being leveraged every day to predict surgical case length more accurately at case posting time and could be potentially utilized to deploy future machine learning models.
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    Wnt signaling suppresses MAPK-driven proliferation of intestinal stem cells.
    (The Journal of clinical investigation, 2018-08) Kabiri, Zahra; Greicius, Gediminas; Zaribafzadeh, Hamed; Hemmerich, Amanda; Counter, Christopher M; Virshup, David M
    Intestinal homeostasis depends on a slowly proliferating stem cell compartment in crypt cells, followed by rapid proliferation of committed progenitor cells in the transit amplifying (TA) compartment. The balance between proliferation and differentiation in intestinal stem cells (ISCs) is regulated by Wnt/β-catenin signaling, although the mechanism remains unclear. We previously targeted PORCN, an enzyme essential for all Wnt secretion, and demonstrated that stromal production of Wnts was required for intestinal homeostasis. Here, a PORCN inhibitor was used to acutely suppress Wnt signaling. Unexpectedly, the treatment induced an initial burst of proliferation in the stem cell compartment of the small intestine, due to conversion of ISCs into TA cells with a loss of intrinsic ISC self-renewal. This process involved MAPK pathway activation, as the proliferating cells in the base of the intestinal crypt contained phosphorylated ERK1/2, and a MEK inhibitor attenuated the proliferation of ISCs and their differentiation into TA cells. These findings suggest a role for Wnt signaling in suppressing the MAPK pathway at the crypt base to maintain a pool of ISCs. The interaction between Wnt and MAPK pathways in vivo has potential therapeutic applications in cancer and regenerative medicine.