Personalized Trial for Chronic Lower Back Pain

The Personalized Trial for Chronic Lower Back Pain will facilitate remote N-of-1 interventions to research participants with self-identified back pain persisting longer than 12 weeks. Participants will be randomized in a multiple crossover design to receive Swedish massage, yoga, and no intervention/usual care while being evaluated with pain intensity, pain interference, fatigue and stress self-reported measures. At the end of the study, participants will receive a personalized report summarizing their observed data in each treatment period

Personalized Trial for Chronic Lower Back Pain

The Personalized Trial for Chronic Lower Back Pain will facilitate remote N-of-1 interventions to research participants with self-identified back pain persisting longer than 12 weeks. Participants will be randomized in a multiple crossover design to receive Swedish massage, yoga, and no intervention/usual care while being evaluated with pain intensity, pain interference, fatigue and stress self-reported measures. At the end of the study, participants will receive a personalized report summarizing their observed data in each treatment period

Intubated COVID-19 predictive (ICOP) score for early mortality after intubation in patients with COVID-19

AbstractPatients with coronavirus disease 2019 (COVID-19) can have increased risk of mortality shortly after intubation. The aim of this study is to develop a model using predictors of early mortality after intubation from COVID-19. A retrospective study of 1945 intubated patients with COVID-19 admitted to 12 Northwell hospitals in the greater New York City area was performed. Logistic regression model using backward selection was applied. This study evaluated predictors of 14-day mortality after intubation for COVID-19 patients. The predictors of mortality within 14 days after intubation included older age, history of chronic kidney disease, lower mean arterial pressure or increased dose of required vasopressors, higher urea nitrogen level, higher ferritin, higher oxygen index, and abnormal pH levels. We developed and externally validated an intubated COVID-19 predictive score (ICOP). The area under the receiver operating characteristic curve was 0.75 (95% CI 0.73–0.78) in the derivation cohort and 0.71 (95% CI 0.67–0.75) in the validation cohort; both were significantly greater than corresponding values for sequential organ failure assessment (SOFA) or CURB-65 scores. The externally validated predictive score may help clinicians estimate early mortality risk after intubation and provide guidance for deciding the most effective patient therapies.</jats:p

Machine learning to assist clinical decision-making during the COVID-19 pandemic

Abstract Background The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information. Main body While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for “Emergency ML.” Throughout the patient care pathway, there are opportunities for ML-supported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models. Conclusion This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume. </jats:sec