Low-dose ketamine for children and adolescents with acute sickle cell disease related pain: A single center experience

Background: Opioids are the mainstay of therapy for painful vasoocclusive episodes (VOEs) in sickle cell disease (SCD). Based on limited studies, low-dose ketamine could be a useful adjuvant analgesic for refractory SCD pain, but its safety and efficacy has not been evaluated in pediatric SCD. Procedure: Using retrospective chart review we recorded and compared characteristics of hospitalizations of 33 children with SCD hospitalized with VOE who were treated with low-dose ketamine and opioid PCA vs. a paired hospitalization where the same patients received opioid PCA without ketamine. We seek to 1) describe a single center experience using adjuvant low-dose ketamine with opioid PCA for sickle cell related pain, 2) retrospectively explore the safety and efficacy of adjuvant low-dose ketamine for pain management, and 3) determine ketamine’s effect on opioid consumption in children and adolescents hospitalized with VOE. Results: During hospitalizations where patients received ketamine, pain scores and opioid use were higher (6.48 vs. 5.99; p=0.002 and 0.040 mg/kg/h vs. 0.032 mg/kg/h; p=0.004 respectively) compared to hospitalizations without ketamine. In 3 patients, ketamine was discontinued due to temporary and reversible psychotomimetic effects. There were no additional short term side effects of ketamine. Conclusions: Low-dose ketamine has an acceptable short-term safety profile for patients with SCD hospitalized for VOE. Lack of an opioid sparing effect of ketamine likely represents use of low-dose ketamine for patients presenting with more severe VOE pain. Prospective randomized studies of adjuvant low-dose ketamine for SCD pain are warranted to determine efficacy and long-term safety

Machine Learning for Prediction of Patients on Hemodialysis with an Undetected SARS-CoV-2 Infection

BACKGROUND: We developed a machine learning (ML) model that predicts the risk of a patient on hemodialysis (HD) having an undetected SARS-CoV-2 infection that is identified after the following ≥3 days. METHODS: As part of a healthcare operations effort, we used patient data from a national network of dialysis clinics (February–September 2020) to develop an ML model (XGBoost) that uses 81 variables to predict the likelihood of an adult patient on HD having an undetected SARS-CoV-2 infection that is identified in the subsequent ≥3 days. We used a 60%:20%:20% randomized split of COVID-19–positive samples for the training, validation, and testing datasets. RESULTS: We used a select cohort of 40,490 patients on HD to build the ML model (11,166 patients who were COVID-19 positive and 29,324 patients who were unaffected controls). The prevalence of COVID-19 in the cohort (28% COVID-19 positive) was by design higher than the HD population. The prevalence of COVID-19 was set to 10% in the testing dataset to estimate the prevalence observed in the national HD population. The threshold for classifying observations as positive or negative was set at 0.80 to minimize false positives. Precision for the model was 0.52, the recall was 0.07, and the lift was 5.3 in the testing dataset. Area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) for the model was 0.68 and 0.24 in the testing dataset, respectively. Top predictors of a patient on HD having a SARS-CoV-2 infection were the change in interdialytic weight gain from the previous month, mean pre-HD body temperature in the prior week, and the change in post-HD heart rate from the previous month. CONCLUSIONS: The developed ML model appears suitable for predicting patients on HD at risk of having COVID-19 at least 3 days before there would be a clinical suspicion of the disease

Machine Learning for Prediction of Hemodialysis Patients with an Undetected SARS-CoV-2 Infection

BACKGROUND: We developed a machine learning (ML) model that predicts the risk of a patient on hemodialysis (HD) having an undetected SARS-CoV-2 infection that is identified after the following ≥3 days. METHODS: As part of a healthcare operations effort, we used patient data from a national network of dialysis clinics (February–September 2020) to develop an ML model (XGBoost) that uses 81 variables to predict the likelihood of an adult patient on HD having an undetected SARS-CoV-2 infection that is identified in the subsequent ≥3 days. We used a 60%:20%:20% randomized split of COVID-19–positive samples for the training, validation, and testing datasets. RESULTS: We used a select cohort of 40,490 patients on HD to build the ML model (11,166 patients who were COVID-19 positive and 29,324 patients who were unaffected controls). The prevalence of COVID-19 in the cohort (28% COVID-19 positive) was by design higher than the HD population. The prevalence of COVID-19 was set to 10% in the testing dataset to estimate the prevalence observed in the national HD population. The threshold for classifying observations as positive or negative was set at 0.80 to minimize false positives. Precision for the model was 0.52, the recall was 0.07, and the lift was 5.3 in the testing dataset. Area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) for the model was 0.68 and 0.24 in the testing dataset, respectively. Top predictors of a patient on HD having a SARS-CoV-2 infection were the change in interdialytic weight gain from the previous month, mean pre-HD body temperature in the prior week, and the change in post-HD heart rate from the previous month. CONCLUSIONS: The developed ML model appears suitable for predicting patients on HD at risk of having COVID-19 at least 3 days before there would be a clinical suspicion of the disease

Machine Learning for Prediction of Patients on Hemodialysis with an Undetected SARS-CoV-2 Infection

Background: We developed a machine learning (ML) model that predicts the risk of a patient on hemodialysis (HD) having an undetected SARS-CoV-2 infection that is identified after the following ≥3 days. Methods: As part of a healthcare operations effort, we used patient data from a national network of dialysis clinics (February-September 2020) to develop an ML model (XGBoost) that uses 81 variables to predict the likelihood of an adult patient on HD having an undetected SARS-CoV-2 infection that is identified in the subsequent ≥3 days. We used a 60%:20%:20% randomized split of COVID-19-positive samples for the training, validation, and testing datasets. Results: We used a select cohort of 40,490 patients on HD to build the ML model (11,166 patients who were COVID-19 positive and 29,324 patients who were unaffected controls). The prevalence of COVID-19 in the cohort (28% COVID-19 positive) was by design higher than the HD population. The prevalence of COVID-19 was set to 10% in the testing dataset to estimate the prevalence observed in the national HD population. The threshold for classifying observations as positive or negative was set at 0.80 to minimize false positives. Precision for the model was 0.52, the recall was 0.07, and the lift was 5.3 in the testing dataset. Area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) for the model was 0.68 and 0.24 in the testing dataset, respectively. Top predictors of a patient on HD having a SARS-CoV-2 infection were the change in interdialytic weight gain from the previous month, mean pre-HD body temperature in the prior week, and the change in post-HD heart rate from the previous month. Conclusions: The developed ML model appears suitable for predicting patients on HD at risk of having COVID-19 at least 3 days before there would be a clinical suspicion of the disease