Unsupervised Machine Learning of the Combined Danish and Norwegian Knee Ligament Registers: Identification of 5 Distinct Patient Groups With Differing ACL Revision Rates.

Background: Most clinical machine learning applications use a supervised learning approach using labeled variables. In contrast, unsupervised learning enables pattern detection without a prespecified outcome.

Purpose/hypothesis: The purpose of this study was to apply unsupervised learning to the combined Danish and Norwegian knee ligament register (KLR) with the goal of detecting distinct subgroups.

External validation of the Norwegian anterior cruciate ligament reconstruction revision prediction model using patients from the STABILITY 1 Trial.

Purpose: A machine learning-based anterior cruciate ligament (ACL) revision prediction model has been developed using Norwegian Knee Ligament Register (NKLR) data, but lacks external validation outside Scandinavia. This study aimed to assess the external validity of the NKLR model (https://swastvedt.shinyapps.

An intersectional framework for counterfactual fairness in risk prediction.

Along with the increasing availability of health data has come the rise of data-driven models to inform decision making and policy. These models have the potential to benefit both patients and health care providers but can also exacerbate health inequities. Existing "algorithmic fairness" methods for measuring and correcting model bias fall short of what is needed for health policy in two key ways.

Ceiling Effect of the Combined Norwegian and Danish Knee Ligament Registers Limits Anterior Cruciate Ligament Reconstruction Outcome Prediction.

Background: Clinical tools based on machine learning analysis now exist for outcome prediction after primary anterior cruciate ligament reconstruction (ACLR). Relying partly on data volume, the general principle is that more data may lead to improved model accuracy.

Purpose/hypothesis: The purpose was to apply machine learning to a combined data set from the Norwegian and Danish knee ligament registers (NKLR and DKRR, respectively), with the aim of producing an algorithm that can predict revision surgery with improved accuracy relative to a previously published model developed using only the NKLR.

Predicting subjective failure of ACL reconstruction: a machine learning analysis of the Norwegian Knee Ligament Register and patient reported outcomes.

Objectives: Accurate prediction of outcome following anterior cruciate ligament (ACL) reconstruction is challenging, and machine learning has the potential to improve our predictive capability. The purpose of this study was to determine if machine learning analysis of the Norwegian Knee Ligament Register (NKLR) can (1) identify the most important risk factors associated with subjective failure of ACL reconstruction and (2) develop a clinically meaningful calculator for predicting the probability of subjective failure following ACL reconstruction.

Methods: Machine learning analysis was performed on the NKLR.

Limited clinical utility of a machine learning revision prediction model based on a national hip arthroscopy registry.

Purpose: Accurate prediction of outcome following hip arthroscopy is challenging and machine learning has the potential to improve our predictive capability. The purpose of this study was to determine if machine learning analysis of the Danish Hip Arthroscopy Registry (DHAR) can develop a clinically meaningful calculator for predicting the probability of a patient undergoing subsequent revision surgery following primary hip arthroscopy.

Methods: Machine learning analysis was performed on the DHAR.

Machine learning algorithm to predict anterior cruciate ligament revision demonstrates external validity.

Purpose: External validation of machine learning predictive models is achieved through evaluation of model performance on different groups of patients than were used for algorithm development. This important step is uncommonly performed, inhibiting clinical translation of newly developed models. Machine learning analysis of the Norwegian Knee Ligament Register (NKLR) recently led to the development of a tool capable of estimating the risk of anterior cruciate ligament (ACL) revision ( https://swastvedt.

Predicting Anterior Cruciate Ligament Reconstruction Revision: A Machine Learning Analysis Utilizing the Norwegian Knee Ligament Register.

Background: Several factors are associated with an increased risk of anterior cruciate ligament (ACL) reconstruction revision. However, the ability to accurately translate these factors into a quantifiable risk of revision at a patient-specific level has remained elusive. We sought to determine if machine learning analysis of the Norwegian Knee Ligament Register (NKLR) can identify the most important risk factors associated with subsequent revision of primary ACL reconstruction and develop a clinically meaningful calculator for predicting revision of primary ACL reconstruction.

Did drug use increase following COVID-19 relaxation of methadone take-out regulations? 2020 was a complicated year.

Background: Relaxation of federal regulations for methadone take-out dosing during the COVID-19 pandemic is unprecedented. The impact of this change on drug use is unknown. This study explores the impact of the federal take-out variance on drug use in one urban opioid treatment program as measured by drug testing.

The Association of Smoking and Alcohol Abuse on Anxiety and Depression in Patients With Recurrent Acute or Chronic Pancreatitis Undergoing Total Pancreatectomy and Islet Autotransplantation: A Report From the Prospective Observational Study of TPIAT Cohort.

Objectives: Smoking and alcohol use are risk factors for acute and chronic pancreatitis, and their role on anxiety, depression, and opioid use in patients who undergo total pancreatectomy and islet autotransplantation (TPIAT) is unknown.

Methods: We included adults enrolled in the Prospective Observational Study of TPIAT (POST). Measured variables included smoking (never, former, current) and alcohol abuse or dependency history (yes vs no).

Portal Vein Thrombosis May Be More Strongly Associated With Islet Infusion Than Extreme Thrombocytosis After Total Pancreatectomy With Islet Autotransplantation.

Background: Total pancreatectomy with islet autotransplantation (TPIAT) involves pancreatectomy, splenectomy, and reinjection of the patient's pancreatic islets into the portal vein. This process triggers a local inflammatory reaction and increase in portal pressure, threatening islet survival and potentially causing portal vein thrombosis. Recent research has highlighted a high frequency of extreme thrombocytosis (platelets ≥1000 × 109/L) after TPIAT, but its cause and association with thrombotic risk remain unclear.

Metabolic measures before surgery and long-term diabetes outcomes in recipients of total pancreatectomy and islet autotransplantation.

In this single-center, retrospective cohort study, we aimed to elucidate simple metabolic markers or surrogate indices of β-cell function that best predict long-term insulin independence and goal glycemic HbA1c control (HbA1c ≤ 6.5%) after total pancreatectomy with islet autotransplantation (TP-IAT). Patients who underwent TP-IAT (n = 371) were reviewed for metabolic measures before TP-IAT and for insulin independence and glycemic control at 1, 3, and 5 years after TP-IAT.