Application of a Machine Learning Algorithm to Develop and Validate a Prediction Model for Ambulatory Non-Arrivals.

BACKGROUND: Non-arrivals to scheduled ambulatory visits are common and lead to a discontinuity of care, poor health outcomes, and increased subsequent healthcare utilization. Reducing non-arrivals is important given their association with poorer health outcomes and cost to health systems. OBJECTIVE: To develop and validate a prediction model for ambulatory non-arrivals. DESIGN: Retrospective cohort study. PATIENTS OR SUBJECTS: Patients at an integrated health system who had an outpatient visit scheduled from January 1, 2020, to February 28, 2022. MAIN MEASURES: Non-arrivals to scheduled appointments. KEY RESULTS: There were over 4.3 million ambulatory appointments from 1.2 million adult patients. Patients with appointment non-arrivals were more likely to be single, racial/ethnic minorities, and not having an established primary care provider compared to those who arrived at their appointments. A prediction model using the XGBoost machine learning algorithm had the highest AUC value (0.768 [0.767-0.770]). Using SHAP values, the most impactful features in the model include rescheduled appointments, lead time (number of days from scheduled to appointment date), appointment provider, number of days since last appointment with the same department, and a patient's prior appointment status within the same department. Scheduling visits close to an appointment date is predicted to be less likely to result in a non-arrival. Overall, the prediction model calibrated well for each department, especially over the operationally relevant probability range of 0 to 40%. Departments with fewer observations and lower non-arrival rates generally had a worse calibration. CONCLUSIONS: Using a machine learning algorithm, we developed a prediction model for non-arrivals to scheduled ambulatory appointments usable for all medical specialties. The proposed prediction model can be deployed within an electronic health system or integrated into other dashboards to reduce non-arrivals. Future work will focus on the implementation and application of the model to reduce non-arrivals.

Prevalence and Predictors of Venous Thromboembolism or Mortality in Hospitalized COVID-19 Patients.

BACKGROUND: We aimed to identify the prevalence and predictors of venous thromboembolism (VTE) or mortality in hospitalized coronavirus disease 2019 (COVID-19) patients. METHODS: A retrospective cohort study of hospitalized adult patients admitted to an integrated health care network in the New York metropolitan region between March 1, 2020 and April 27, 2020. The final analysis included 9,407 patients with an overall VTE rate of 2.9% (2.4% in the medical ward and 4.9% in the intensive care unit [ICU]) and a VTE or mortality rate of 26.1%. Most patients received prophylactic-dose thromboprophylaxis. Multivariable analysis showed significantly reduced VTE or mortality with Black race, history of hypertension, angiotensin converting enzyme/angiotensin receptor blocker use, and initial prophylactic anticoagulation. It also showed significantly increased VTE or mortality with age 60 years or greater, Charlson Comorbidity Index (CCI) of 3 or greater, patients on Medicare, history of heart failure, history of cerebrovascular disease, body mass index greater than 35, steroid use, antirheumatologic medication use, hydroxychloroquine use, maximum D-dimer four times or greater than the upper limit of normal (ULN), ICU level of care, increasing creatinine, and decreasing platelet counts. CONCLUSION: In our large cohort of hospitalized COVID-19 patients, the overall in-hospital VTE rate was 2.9% (4.9% in the ICU) and a VTE or mortality rate of 26.1%. Key predictors of VTE or mortality included advanced age, increasing CCI, history of cardiovascular disease, ICU level of care, and elevated maximum D-dimer with a cutoff at least four times the ULN. Use of prophylactic-dose anticoagulation but not treatment-dose anticoagulation was associated with reduced VTE or mortality.

Presence of early CKD-related metabolic complications predict progression of stage 3 CKD: a case-controlled study.

BACKGROUND: Only a subset of patients who enter stage 3 chronic kidney disease (CKD) progress to stage 4. Identifying which patients entering stage 3 are most likely to progress could improve outcomes, by allowing more appropriate referrals for specialist care, and spare those unlikely to progress the adverse effects and costliness of an unnecessarily aggressive approach. We hypothesized that compared to non-progressors, patients who enter stage 3 CKD and ultimately progress have experienced greater loss of renal function, manifested by impairment of metabolic function (anemia, worsening acidosis and mineral abnormalities), than is reflected in the eGFR at entry to stage 3. The purpose of this case-controlled study was to design a prediction model for CKD progression using laboratory values reflecting metabolic status. METHODS: Using data extracted from the electronic health record (EHR), two cohorts of patients in stage 3 were identified: progressors (eGFR declined >3 ml/min/1.73 m2/year; n=117) and non-progressors (eGFR declined <1 ml/min/1.713 m2; n=364). Initial laboratory values recorded a year before to a year after the time of entry to stage 3, reflecting metabolic complications (hemoglobin, bicarbonate, calcium, phosphorous, and albumin) were obtained. Average values in progressors and non-progressors were compared. Classification algorithms (Naïve Bayes and Logistic Regression) were used to develop prediction models of progression based on the initial lab data. RESULTS: At the entry to stage 3 CKD, hemoglobin, bicarbonate, calcium, and albumin values were significantly lower and phosphate values significantly higher in progressors compared to non-progressors even though initial eGFR values were similar. The differences were sufficiently large that a prediction model of progression could be developed based on these values. Post-test probability of progression in patients classified as progressors or non-progressors were 81% (73% - 86%) and 17% (13% - 23%), respectively. CONCLUSIONS: Our studies demonstrate that patients who enter stage 3 and ultimately progress to stage 4 manifest a greater degree of metabolic complications than those who remain stable at the onset of stage 3 when eGFR values are equivalent. Lab values (hemoglobin, bicarbonate, phosphorous, calcium and albumin) are sufficiently different between the two cohorts that a reasonably accurate predictive model can be developed.