Suchergebnisse

BACKGROUND: PATHFx is a clinical decision-support tool based on machine learning capable of estimating the likelihood of survival after surgery for patients with skeletal metastases. The applicability of any machine-learning tool depends not only on successful external validation in unique patient populations but also on remaining relevant as more effective systemic treatments are introduced. With advancements in the treatment of metastatic disease, it is our responsibility to patients to ensure clinical support tools remain contemporary and accurate. QUESTION/PURPOSES: Therefore, we sought to (1) generate updated PATHFx models using recent data from patients treated at one large, urban tertiary referral center and (2) externally validate the models using two contemporary patient populations treated either surgically or nonsurgically with external-beam radiotherapy alone for symptomatic skeletal metastases for symptomatic lesions. METHODS: After obtaining institutional review board approval, we collected data on 208 patients undergoing surgical treatment for pathologic fractures at Memorial Sloan Kettering Cancer Center between 2015 and 2018. These data were combined with the original PATHFx training set (n = 189) to create the final training set (n = 397). We then created six Bayesian belief networks designed to estimate the likelihood of 1-month, 3-month, 6-month, 12-month, 18-month, and 24-month survival after treatment. Bayesian belief analysis is a statistical method that allows data-driven learning to arise from conditional probabilities by exploring relationships between variables to estimate the likelihood of an outcome using observed data. For external validation, we extracted the records of patients treated between 2016 and 2018 from the International Bone Metastasis Registry and records of patients treated nonoperatively with external-beam radiation therapy for symptomatic skeletal metastases from 2012 to 2016 using the Military Health System Data Repository (radiotherapy-only group). From each ...

BACKGROUND: Limited data are available regarding excessive opioid prescribing in the perioperative period after routine orthopaedic procedures in US military personnel. PURPOSE: To examine the demographic profile of the patients receiving these medications and to identify potential risk factors for prolonged opioid use after anterior cruciate ligament reconstruction (ACLR) in the active duty military population. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: The Military Analysis and Reporting Tool (M2) was used to search the Military Health System Data Repository (MDR) for patients undergoing ACLR from 2012 through 2015 and specifically for active duty personnel with an arthroscopically assisted ACLR (Current Procedural Terminology [CPT] code 29888). Complete opioid prescription filling history was also obtained. This study had 2 primary outcomes: (1) use of opiate analgesics more than 90 days after surgery, representing prolonged opiate prescriptions, and (2) high levels of postoperative opiate use, defined as having filled prescriptions accounting for greater than the 95th percentile of morphine equivalents for patients in the study cohort. Data were analyzed via multivariate regression analysis to identify potential associations with the primary outcomes. RESULTS: A total of 9474 patients met the inclusion criteria. Median patient age was 27 years, and the sample included 1316 (14%) female and 8158 (86%) male patients. A total of 66 (0.7%) patients had a preoperative diagnosis for substance abuse; 2656 (28%) patients continued to receive opioid prescriptions more than 90 days after surgery, and 502 (5%) patients were in the top 95th percentile of all opioid users within the study cohort. Total preoperative morphine equivalents per day and total perioperative morphine equivalents per day were highly important risk factors for both outcomes, although other demographic factors such as race, sex, and age may play minor roles. CONCLUSION: We identified total preoperative morphine equivalents, ...

BACKGROUND: Machine-learning methods such as the Bayesian belief network, random forest, gradient boosting machine, and decision trees have been used to develop decision-support tools in other clinical settings. Opioid abuse is a problem among civilians and military service members, and it is difficult to anticipate which patients are at risk for prolonged opioid use. QUESTIONS/PURPOSES: (1) To build a cross-validated model that predicts risk of prolonged opioid use after a specific orthopaedic procedure (ACL reconstruction), (2) To describe the relationships between prognostic and outcome variables, and (3) To determine the clinical utility of a predictive model using a decision curve analysis (as measured by our predictive system's ability to effectively identify high-risk patients and allow for preventative measures to be taken to ensure a successful procedure process). METHODS: We used the Military Analysis and Reporting Tool (M2) to search the Military Health System Data Repository for all patients undergoing arthroscopically assisted ACL reconstruction (Current Procedure Terminology code 29888) from January 2012 through December 2015 with a minimum of 90 days postoperative follow-up. In total, 10,919 patients met the inclusion criteria, most of whom were young men on active duty. We obtained complete opioid prescription filling histories from the Military Health System Data Repository's pharmacy records. We extracted data including patient demographics, military characteristics, and pharmacy data. A total of 3.3% of the data was missing. To curate and impute all missing variables, we used a random forest algorithm. We shuffled and split the data into 80% training and 20% hold-out sets, balanced by outcome variable (Outcome90Days). Next, the training set was further split into training and validation sets. Each model was built on the training data set, tuned with the validation set as applicable, and finally tested on the separate hold-out dataset. We chose four predictive models to develop, at the end ...

INTRODUCTION: Invasive fungal infections (IFI) are associated with high morbidity and mortality. A better method of risk stratifying trauma patients for combat-related IFI is needed to improve clinical outcomes while minimizing morbidity related to overtreatment. We sought to develop combat-related IFI clinical decision support (CDS) tools to assist providers to make treatment decisions both near the point of injury and subsequently at definitive treatment centers. MATERIALS AND METHODS: We utilized a training dataset containing information from 227 combat-injured military personnel to build a Bayesian belief network (BBN) to predict the likelihood of developing IFI using information available at the point of initial resuscitation (THEATER model) and in the tertiary care setting (MEDCEN model). After selecting BBN models, external validation used a separate test dataset of 350 wounded warriors. Furthermore, the performance of the BBN models was compared with a "two-rule model" alone (based on physician experience) and combinations of the BBN models plus the two-rule model. The two-rule model contains plausible IFI criteria, but it has not been formally evaluated, and they are not currently actual clinical guidelines. RESULTS: We found receiver operating characteristic areas under the curve (AUC) of 0.70 (95% CI: [0.62, 0.77]) and 0.68 (95% CI: [0.59, 0.76]) for the THEATER and MEDCEN BBN models, respectively, on cross-validation. External validation with the highest AUC BBN models produced THEATER AUC of 0.68 (95% CI: [0.58, 0.78]) and MEDCEN AUC of 0.67 (95% CI: [0.57, 0.78]). With the incorporation of two-rule model in low IFI-prevalence populations, external validation AUC increased to 0.77 (95% CI: [0.69, 0.84]) for the THEATER model and 0.76 (95% CI:[0.68, 0.85]) for the LRMC model. The two-rule model alone has an AUC of 0.72 (95% CI: [0.63, 0.81]). CONCLUSIONS: Overall, the IFI tools produced clinically useful, robust models. However, the clinical utility of these models is highly dependent upon the ...

Post-traumatic heterotopic ossification (HO) is the formation of ectopic bone in non-osseous structures following injury. The precise mechanism for bone development following trauma is unknown; however, early onset of HO may involve the production of pro-osteogenic serum factors. Here we evaluated serum from a cohort of civilian and military patients post trauma to determine early induction gene signatures in orthopaedic trauma induced HO. To test this, human adipose derived stromal/stem cells (hASCs) were stimulated with human serum from patients who developed HO following trauma and evaluated for a gene panel with qPCR. Pathway gene analysis ontology revealed that hASCs stimulated with serum from patients who developed HO had altered gene expression in the activator protein 1 (AP1) and AP1 transcriptional targets pathways. Notably, there was a significant repression in FOS gene expression in hASCs treated with serum from individuals with HO. Furthermore, the mitogen-activated protein kinase (MAPK) signaling pathway was activated in hASCs following serum exposure from individuals with HO. Serum from both military and civilian patients with trauma induced HO had elevated downstream genes associated with the MAPK pathways. Stimulation of hASCs with known regulators of osteogenesis (BMP2, IL6, Forskolin, and WNT3A) failed to recapitulate the gene signature observed in hASCs following serum stimulation, suggesting non-canonical mechanisms for gene regulation in trauma induced HO. These findings provide new insight for the development of HO and support ongoing work linking the systemic response to injury with wound specific outcomes.