Evaluation of shelter dog activity levels before and during COVID-19 using automated analysis.

Animal shelters have been found to represent stressful environments for pet dogs, both affecting behavior and influencing welfare. The current COVID-19 pandemic has brought to light new uncertainties in animal sheltering practices which may affect shelter dog behavior in unexpected ways. To evaluate this, we analyzed changes in dog activity levels before COVID-19 and during COVID-19 using an automated video analysis within a large, open-admission animal shelter in New York City, USA. Shelter dog activity was analyzed during two two-week long time periods: (i) just before COVID-19 safety measures were put in place (Feb 26-Mar 17, 2020) and (ii) during the COVID-19 quarantine (July 10-23, 2020). During these two periods, video clips of 15.3 second, on average, were taken of participating kennels every hour from approximately 8 am to 8 pm. Using a two-step filtering approach, a matched sample (based on the number of days of observation) of 34 dogs was defined, consisting of 17 dogs in each group (N1/N2 = 17). An automated video analysis of active/non-active behaviors was conducted and compared to manual coding of activity. The automated analysis validated by comparison to manual coding reaching above 79% accuracy. Significant differences in the patterns of shelter dog activity were observed: less activity was observed in the afternoons before COVID-19 restrictions, while during COVID-19, activity remained at a constant average. Together, these findings suggest that 1) COVID-19 lockdown altered shelter dog in-kennel activity, likely due to changes in the shelter environment and 2) automated analysis can be used as a hands-off tool to monitor activity. While this method of analysis presents immense opportunity for future research, we discuss the limitations of automated analysis and guidelines in the context of shelter dogs that can increase accuracy of detection, as well as reflect on policy changes that might be helpful in mediating canine stress in changing shelter environments.

Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients.

Recent health reforms have created incentives for cardiologists and accountable care organizations to participate in value-based care models for heart failure (HF). Accurate risk stratification of HF patients is critical to efficiently deploy interventions aimed at reducing preventable utilization. The goal of this paper was to compare deep learning approaches with traditional logistic regression (LR) to predict preventable utilization among HF patients. We conducted a prognostic study using data on 93,260 HF patients continuously enrolled for 2-years in a large U.S. commercial insurer to develop and validate prediction models for three outcomes of interest: preventable hospitalizations, preventable emergency department (ED) visits, and preventable costs. Patients were split into training, validation, and testing samples. Outcomes were modeled using traditional and enhanced LR and compared to gradient boosting model and deep learning models using sequential and non-sequential inputs. Evaluation metrics included precision (positive predictive value) at k, cost capture, and Area Under the Receiver operating characteristic (AUROC). Deep learning models consistently outperformed LR for all three outcomes with respect to the chosen evaluation metrics. Precision at 1% for preventable hospitalizations was 43% for deep learning compared to 30% for enhanced LR. Precision at 1% for preventable ED visits was 39% for deep learning compared to 33% for enhanced LR. For preventable cost, cost capture at 1% was 30% for sequential deep learning, compared to 18% for enhanced LR. The highest AUROCs for deep learning were 0.778, 0.681 and 0.727, respectively. These results offer a promising approach to identify patients for targeted interventions.

Development of a machine learning algorithm for early detection of opioid use disorder.

BACKGROUND: Opioid use disorder (OUD) affects an estimated 16 million people worldwide. The diagnosis of OUD is commonly delayed or missed altogether. We aimed to test the utility of machine learning in creating a prediction model and algorithm for early diagnosis of OUD. SUBJECTS AND METHODS: We analyzed data gathered in a commercial claim database from January 1, 2006, to December 31, 2018 of 10 million medical insurance claims from 550 000 patient records. We compiled 436 predictor candidates, divided to six feature groups - demographics, chronic conditions, diagnosis and procedures features, medication features, medical costs, and episode counts. We employed the Word2Vec algorithm and the Gradient Boosting trees algorithm for the analysis. RESULTS: The c-statistic for the model was 0.959, with a sensitivity of 0.85 and specificity of 0.882. Positive Predictive Value (PPV) was 0.362 and Negative Predictive Value (NPV) was 0.998. Significant differences between positive OUD- and negative OUD- controls were in the mean annual amount of opioid use days, number of overlaps in opioid prescriptions per year, mean annual opioid prescriptions, and annual benzodiazepine and muscle relaxant prescriptions. Notable differences were the count of intervertebral disc disorder-related complaints per year, post laminectomy syndrome diagnosed per year, and pain disorders diagnosis per year. Significant differences were also found in the episodes and costs categories. CONCLUSIONS: The new algorithm offers a mean 14.4 months reduction in time to diagnosis of OUD, at potential saving in further morbidity, medical cost, addictions and mortality.

Machine learning algorithm for early detection of end-stage renal disease.

BACKGROUND: End stage renal disease (ESRD) describes the most severe stage of chronic kidney disease (CKD), when patients need dialysis or renal transplant. There is often a delay in recognizing, diagnosing, and treating the various etiologies of CKD. The objective of the present study was to employ machine learning algorithms to develop a prediction model for progression to ESRD based on a large-scale multidimensional database. METHODS: This study analyzed 10,000,000 medical insurance claims from 550,000 patient records using a commercial health insurance database. Inclusion criteria were patients over the age of 18 diagnosed with CKD Stages 1-4. We compiled 240 predictor candidates, divided into six feature groups: demographics, chronic conditions, diagnosis and procedure features, medication features, medical costs, and episode counts. We used a feature embedding method based on implementation of the Word2Vec algorithm to further capture temporal information for the three main components of the data: diagnosis, procedures, and medications. For the analysis, we used the gradient boosting tree algorithm (XGBoost implementation). RESULTS: The C-statistic for the model was 0.93 [(0.916-0.943) 95% confidence interval], with a sensitivity of 0.715 and specificity of 0.958. Positive Predictive Value (PPV) was 0.517, and Negative Predictive Value (NPV) was 0.981. For the top 1 percentile of patients identified by our model, the PPV was 1.0. In addition, for the top 5 percentile of patients identified by our model, the PPV was 0.71. All the results above were tested on the test data only, and the threshold used to obtain these results was 0.1. Notable features contributing to the model were chronic heart and ischemic heart disease as a comorbidity, patient age, and number of hypertensive crisis events. CONCLUSIONS: When a patient is approaching the threshold of ESRD risk, a warning message can be sent electronically to the physician, who will initiate a referral for a nephrology consultation to ensure an investigation to hasten the establishment of a diagnosis and initiate management and therapy when appropriate.

Percutaneous aspiration of the gall bladder for the treatment of acute cholecystitis: a prospective study.

BACKGROUND: Urgent laparoscopic cholecystectomy has been established as the best treatment for acute cholecystitis. However, conservative treatment is advocated for high-risk patients. Failure of conservative treatment can result in high-risk operations with relatively high rates of operative morbidity. Percutaneous cholecystostomy is a good option for these patients. Recently, percutaneous aspiration of the gall bladder without drain has been described. METHODS: A protocol of initial conservative management in high-operative-risk patients admitted with acute cholecystitis was prospectively assessed. Patients who did not respond to antibiotics were treated with percutaneous trans-hepatic aspiration of the gall bladder under ultrasound guidance. Following discharge, the patients were seen in the outpatient clinic and elective laparoscopic cholecystectomy was considered and scheduled as necessary. RESULTS: Between January 2011 and December 2012, 33 patients with persistent clinical and sonographic signs of acute cholecystitis after failure of initial antibiotic treatment underwent gall bladder aspiration under ultrasound guidance. No complications related to the procedure were reported. In 25 patients (76 %), the procedure was successful and they were discharged. Seven patients needed repeated aspiration. Eight patients (24 %) who did not improve underwent percutaneous cholecystostomy and were discharged with a drain and later reevaluated for elective surgery. The mean hospital stay of patients with successful aspiration was 3 days. During the follow-up period, 23 patients underwent elective interval laparoscopic cholecystectomy. Two were converted to open surgery (8.7 %). CONCLUSIONS: Conservative treatment and delayed operation is an acceptable option for acute cholecystitis. Percutaneous gall bladder aspiration is a simple and effective procedure, with a high success rate and low morbidity. Laparoscopic cholecystectomy after drainage of the gall bladder has low morbidity with a relatively low conversion rate.