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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. The primary outcome for the models was probability of revision hip arthroscopy within 1, 2, and/or 5 years after primary hip arthroscopy. Data were split randomly into training (75%) and test (25%) sets. Four models intended for these types of data were tested: Cox elastic net, random survival forest, gradient boosted regression (GBM), and super learner. These four models represent a range of approaches to statistical details like variable selection and model complexity. Model performance was assessed by calculating calibration and area under the curve (AUC). Analysis was performed using only variables available in the pre-operative clinical setting and then repeated to compare model performance using all variables available in the registry. Results In total, 5581 patients were included for analysis. Average follow-up time or time-to-revision was 4.25 years (± 2.51) years and overall revision rate was 11%. All four models were generally well calibrated and demonstrated concordance in the moderate range when restricted to only pre-operative variables (0.62–0.67), and when considering all variables available in the registry (0.63–0.66). The 95% confidence intervals for model concordance were wide for both analyses, ranging from a low of 0.53 to a high of 0.75, indicating uncertainty about the true accuracy of the models. Conclusion The association between pre-surgical factors and outcome following hip arthroscopy is complex. Machine learning analysis of the DHAR produced a model capable of predicting revision surgery risk following primary hip arthroscopy that demonstrated moderate accuracy but likely limited clinical usefulness. Prediction accuracy would benefit from enhanced data quality within the registry and this preliminary study holds promise for future model generation as the DHAR matures. Ongoing collection of high-quality data by the DHAR should enable improved patient-specific outcome prediction that is generalisable across the population. Level of evidence Level III.

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. METHODS This retrospective single-site study of a contemporary cohort of 409 pediatric and adult patients analyzed the frequency of thrombocytosis, risk factors for thrombosis, and antiplatelet and anticoagulation strategies. RESULTS Of 409 patients, 67% developed extreme thrombocytosis, peaking around postoperative day 16. Extreme thrombocytosis was significantly associated with infused islet volumes. Thromboembolic events occurred in 12.2% of patients, with portal vein thromboses occurring significantly earlier than peripheral thromboses. Portal vein thromboses were associated with infused islet volumes and portal pressures but not platelet counts or other measures. Most thromboembolic events (82.7%) occurred before the postoperative day of maximum platelet count. Only 4 of 27 (14.8%) of portal vein thromboses occurred at platelet counts ≥500 × 109/L. Perioperative heparin was given to all patients. Treatment of reactive thrombocytosis using aspirin in adults and hydroxyurea in children was not associated with significantly decreased thromboembolic risk. CONCLUSIONS These results suggest that post-TPIAT thrombocytosis and portal vein thromboses may be linked to the islet infusion inflammation, not directly to each other, and further reducing this inflammation may reduce thrombosis and thrombocytosis frequencies simultaneously.

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. Insulin independence and goal glycemic control were achieved in 33% and 68% at 1 year, respectively. Although the groups who were insulin independent and dependent overlap substantially on baseline measures, an individual who has abnormal glycemia (prediabetes HbA1c or fasting glucose) or estimated IEQs/kg < 2500 has a very high likelihood of remaining insulin dependent after surgery. In multivariate logistic regression modelling, metabolic measures correctly predicted insulin independence in about 70% of patients at 1, 3, and 5 years after TP-IAT. In conclusion, metabolic testing measures before surgery are highly associated with diabetes outcomes after TP-IAT at a population level and correctly predict outcomes in approximately two out of three patients. These findings may aid in prognostic counseling for chronic pancreatitis patients who are likely to eventually need TP-IAT.

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.shinyapps.io/calculator_rev/). The purpose of this study was to determine the external validity of the NKLR model by assessing algorithm performance when applied to patients from the Danish Knee Ligament Registry (DKLR). Methods The primary outcome measure of the NKLR model was probability of revision ACL reconstruction within 1, 2, and/or 5 years. For external validation, all DKLR patients with complete data for the five variables required for NKLR prediction were included. The five variables included graft choice, femur fixation device, KOOS QOL score at surgery, years from injury to surgery, and age at surgery. Predicted revision probabilities were calculated for all DKLR patients. The model performance was assessed using the same metrics as the NKLR study: concordance and calibration. Results In total, 10,922 DKLR patients were included for analysis. Average follow-up time or time-to-revision was 8.4 (± 4.3) years and overall revision rate was 6.9%. Surgical technique trends (i.e., graft choice and fixation devices) and injury characteristics (i.e., concomitant meniscus and cartilage pathology) were dissimilar between registries. The model produced similar concordance when applied to the DKLR population compared to the original NKLR test data (DKLR: 0.68; NKLR: 0.68–0.69). Calibration was poorer for the DKLR population at one and five years post primary surgery but similar to the NKLR at two years. Conclusion The NKLR machine learning algorithm demonstrated similar performance when applied to patients from the DKLR, suggesting that it is valid for application outside of the initial patient population. This represents the first machine learning model for predicting revision ACL reconstruction that has been externally validated. Clinicians can use this in-clinic calculator to estimate revision risk at a patient specific level when discussing outcome expectations pre-operatively. While encouraging, it should be noted that the performance of the model on patients undergoing ACL reconstruction outside of Scandinavia remains unknown. Level of evidence III.

Introduction: Neurologically favorable survival following OHCA depends on many factors including patient age, presenting rhythm, bystander CPR, CPR duration, and return of spontaneous circulation (ROSC). This study aims to utilize machine learning to construct predictive models of neurologically favorable survival in OHCA patients receiving standard ACLS and extracorporeal cardiopulmonary resuscitation (ECPR). Methods: 3011 patients from the Amiodarone, Lidocaine, Placebo Study (ALPS) were analyzed using supervised and unsupervised machine learning approaches based on several variables available at the time of hospital presentation after OHCA. Machine learning platforms were developed to create real world predictive models of resuscitation. The model was then refined for 180 patients from the UMN-ECPR study with refractory VF/VT cardiac arrest who underwent ECPR. Resuscitative variables were selected and ranked on the likelihood ratio of predicting survival. Results: Machine learning analysis of the ALPS cohort yielded a model of survival with an area under the receiver-operating characteristic curve (AUC) of 0.92. ROSC, CPR duration and age were the highest determinants of neurologically favorable survival. The model was tested on the UMN-ECPR cohort, showing an AUC of 0.74 with a sensitivity and negative predictive value for survival of 12% and 60%, respectively. Machine learning was again used to render a model tailored for the UMN-ECPR group. The ECPR predictive algorithm had an AUC of 0.83, sensitivity and negative predictive value for survival of 70% and 80%, respectively, in which presenting rhythm to the cath lab, ROSC and CPR duration were the main determinants of neurologically favorable survival. Conclusion: Simple and effective predictive algorithms can predict the outcomes of cardiac arrest using only cardiac arrest characteristics. Use of ECPR changes the predictive determinants substantially. While this information is helpful in the appropriate context, the specificity required to withdraw care or halt resuscitation will require further data points and refinement.

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. Methods Machine learning analysis was performed on the NKLR data set. The primary outcome was the probability of revision ACL reconstruction within 1, 2, and/or 5 years. Data were split randomly into training sets (75%) and test sets (25%). Four machine learning models were tested: Cox Lasso, survival random forest, generalized additive model, and gradient boosted regression. Concordance and calibration were calculated for all 4 models. Results The data set included 24,935 patients, and 4.9% underwent a revision surgical procedure during a mean follow-up (and standard deviation) of 8.1 ± 4.1 years. All 4 models were well-calibrated, with moderate concordance (0.67 to 0.69). The Cox Lasso model required only 5 variables for outcome prediction. The other models either used more variables without an appreciable improvement in accuracy or had slightly lower accuracy overall. An in-clinic calculator was developed that can estimate the risk of ACL revision (Revision Risk Calculator). This calculator can quantify risk at a patient-specific level, with a plausible range from near 0% for low-risk patients to 20% for high-risk patients at 5 years. Conclusions Machine learning analysis of a national knee ligament registry can predict the risk of ACL reconstruction revision with moderate accuracy. This algorithm supports the creation of an in-clinic calculator for point-of-care risk stratification based on the input of only 5 variables. Similar analysis using a larger or more comprehensive data set may improve the accuracy of risk prediction, and future studies incorporating patients who have experienced failure of ACL reconstruction but have not undergone subsequent revision may better predict the true risk of ACL reconstruction failure. Level of evidence Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

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.Machine learning analysis was performed on the NKLR. All patients with 2-year follow-up data were included. The primary outcome was the probability of subjective failure 2 years following primary surgery, defined as a Knee Injury and Osteoarthritis Outcome Score (KOOS) Quality of Life (QoL) subscale score of44. Data were split randomly into training (75%) and test (25%) sets. Four models intended for this type of data were tested: Lasso logistic regression, random forest, generalized additive model (GAM), and gradient boosted regression (GBM). These four models represent a range of approaches to statistical details like variable selection and model complexity. Model performance was assessed by calculating calibration and area under the curve (AUC).Of the 20,818 patients who met the inclusion criteria, 11,630 (56%) completed the 2-year follow-up KOOS QoL questionnaire. Of those with complete KOOS data, 22% reported subjective failure. The lasso logistic regression, GBM, and GAM all demonstrated AUC in the moderate range (0.67-0.68), with the GAM performing best (0.68; 95% CI 0.64-0.71). Lasso logistic regression, GBM, and the GAM were well-calibrated, while the random forest showed evidence of mis-calibration. The GAM was selected to create an in-clinic calculator to predict subjective failure risk at a patient-specific level (https://swastvedt.shinyapps.io/calculator_koosqol/).Machine learning analysis of the NKLR can predict subjective failure risk following ACL reconstruction with fair accuracy. This algorithm supports the creation of an easy-to-use in-clinic calculator for point-of-care risk stratification. Clinicians can use this calculator to estimate subjective failure risk at a patient-specific level when discussing outcome expectations preoperatively.Level-III Retrospective review of a prospective national register.

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). Using univariable and multivariable analyses, we investigated the association of smoking and alcohol dependency history with anxiety and depression, opioid use, and postsurgical outcomes. Results Of 195 adults studied, 25 were current smokers and 77 former smokers, whereas 18 had a history of alcohol dependency (of whom 10 were current smokers). A diagnosis of anxiety was associated with current smoking (P = 0.005), and depression was associated with history of alcohol abuse/dependency (P = 0.0001). However, active symptoms of anxiety and depression at the time of TPIAT were not associated with smoking or alcohol status. Opioid use in the past 14 days was associated with being a former smoker (P = 0.005). Conclusions Active smoking and alcohol abuse history were associated with a diagnosis of anxiety and depression, respectively; however, at the time of TPIAT, symptom scores suggested that they were being addressed.