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Introduction and Objective: The lack of precision to identify patients with early-stage diabetic kidney disease (DKD) at near-term risk for progressive decline in kidney function results in poor disease management often leading to kidney failure requiring unplanned dialysis. The KidneyIntelX is a multiplex, bioprognostic, immunoassay consisting of 3 plasma biomarkers and clinical variables that uses machine learning to generate a risk score for progressive decline in kidney function over 5-year in adults with early-stage DKD. Our objective was to assess the impact of KidneyIntelX on management and outcomes in a Health System in the real-world evidence (RWE) study. Methods: KidneyIntelX was introduced into a large metropolitan Health System via a population health-defined approved care pathway for patients with stages 1 to 3 DKD between [November 2020 to March 2022]. Decision impact on visit frequency, medication management, specialist referral, and selected lab values was assessed. We performed an interim analysis in patients through 6-months post-test date to evaluate the impact of risk level with clinical decision-making and outcomes. Results: A total of 1686 patients were enrolled in the RWE study and underwent KidneyIntelX testing and subsequent care pathway management. The median age was 68 years, 52% were female, 26% self-identified as Black, and 94% had hypertension. The median baseline eGFR was 59 ml/minute/1.73 m2, urine albumin-creatinine ratio was 69 mg/g, and HbA1c was 7.7%. After testing, a clinical encounter in the first month occurred in 13%, 43%, and 53% of low-risk, intermediate-risk, and high-risk patients, respectively and 46%, 61%, and 71% had at least 1 action taken within the first 6 months. High-risk patients were more likely to be placed on SGLT2 inhibitors (OR = 4.56; 95% CI 3.00-6.91 vs low-risk), and more likely to be referred to a specialist such as a nephrologist, endocrinologist, or dietician (OR = 2.49; 95% CI 1.53-4.01) compared to low-risk patients. Conclusions: The combination of KidneyIntelX, clinical guidelines and educational support resulted in changes in clinical management by clinicians. After testing, there was an increase in visit frequency, referrals for disease management, and introduction to guideline-recommended medications. These differed by risk category, indicating an impact of KidneyIntelX risk stratification on clinical care.

Background Data on patients with coronavirus disease 2019 (COVID-19) who return to hospital after discharge are scarce. Characterization of these patients may inform post-hospitalization care. Objective To describe clinical characteristics of patients with COVID-19 who returned to the emergency department (ED) or required readmission within 14 days of discharge. Design Retrospective cohort study of SARS-COV-2-positive patients with index hospitalization between February 27 and April 12, 2020, with ≥ 14-day follow-up. Significance was defined as P < 0.05 after multiplying P by 125 study-wide comparisons. Participants Hospitalized patients with confirmed SARS-CoV-2 discharged alive from five New York City hospitals. Main Measures Readmission or return to ED following discharge. Results Of 2864 discharged patients, 103 (3.6%) returned for emergency care after a median of 4.5 days, with 56 requiring inpatient readmission. The most common reason for return was respiratory distress (50%). Compared with patients who did not return, there were higher proportions of COPD (6.8% vs 2.9%) and hypertension (36% vs 22.1%) among those who returned. Patients who returned also had a shorter median length of stay (LOS) during index hospitalization (4.5 [2.9,9.1] vs 6.7 [3.5, 11.5] days; Padjusted = 0.006), and were less likely to have required intensive care on index hospitalization (5.8% vs 19%; Padjusted = 0.001). A trend towards association between absence of in-hospital treatment-dose anticoagulation on index admission and return to hospital was also observed (20.9% vs 30.9%, Padjusted = 0.06). On readmission, rates of intensive care and death were 5.8% and 3.6%, respectively. Conclusions Return to hospital after admission for COVID-19 was infrequent within 14 days of discharge. The most common cause for return was respiratory distress. Patients who returned more likely had COPD and hypertension, shorter LOS on index-hospitalization, and lower rates of in-hospital treatment-dose anticoagulation. Future studies should focus on whether these comorbid conditions, longer LOS, and anticoagulation are associated with reduced readmissions. Electronic supplementary material The online version of this article (10.1007/s11606-020-06120-6) contains supplementary material, which is available to authorized users.

Objective: This study aims to assess the correlation of exhaled CO and nicotine dependence with the occurrence of oral mucosal lesions while also taking into consideration socio-demographic, clinical and anthropometrical characteristics of participants. Methods: An observational cross-sectional study was carried out among smokers who visited the tobacco cessation center at Tertiary Care Dental Hospital in Goa, India. An intra-oral soft tissue examination for detecting presence of oral mucosal lesions followed by a questionnaire-based interview for the measurement of exposure, sociodemographic factors, body mass index, cooking habits and nicotine dependence was conducted. The exhaled CO levels were measured with a CO breath analyzer. Statistical analysis was performed using IBM SPSS version 20.0 Descriptive statistics were calculated and multivariable analysis was done to assess the association of different variables with oral mucosal lesions and carbon monoxide levels. p-value ≤ 0.05 was considered as statistically significant. Results: Of the 173 subjects enrolled in the study, 69.36% were without any lesions while 30.63% were diagnosed with some lesion. In the regression analysis, the variables of physical activity (present vs absent OR: 5.808), exhaled CO levels (OR: 1.098) and nicotine dependence (mild vs moderate OR: 6.518) were significant risk factors influencing the presence of oral mucosal lesions. Usage of both cigarettes and bidis by smokers exhibited highest mean exhaled CO values of 19.67±1.506 ppm. Exhaled CO levels were significantly higher in smokers who were overweight (14.96±9.14 ppm), physically inactive (13.98±8.26 ppm), highly nicotine dependent (20.67±8.30) and used coal for cooking (12.55±8.17). Conclusion: A robust correlation between exhaled CO levels, nicotine dependence and incidence of oral mucosal lesions was established. The multifactorial tenacity of exhaled CO which is affected by smoked tobacco as well as variables such as physical activity, BMI, cooking habits and type of smoking habit should be noted.

Background COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. Objective The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. Methods We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19–positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Results Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. Conclusions We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

Background: Various medications are cleared by the kidneys, therefore patients with impaired renal function, especially dialysis patients are at risk for adverse drug events (ADEs). There are limited studies on ADEs in maintenance dialysis patients. Methods: We utilized a nationally representative database, the Nationwide Emergency Department Sample, from 2008 to 2013, to compare emergency department (ED) visits for dialysis and propensity matched non-dialysis patients. Log binomial regression was used to calculate relative risk of hospital admission and logistic regression to calculate ORs for in-hospital mortality while adjusting for patient and hospital characteristics. Results: While ED visits for ADEs decreased in both groups, they were over 10-fold higher in dialysis patients than non-dialysis patients (65.8–88.5 per 1,000 patients vs. 4.6–5.4 per 1,000 patients respectively, p < 0.001). The top medication category associated with ED visits for ADEs in dialysis patients is agents primarily affecting blood constituents, which has increased. After propensity matching, patient admission was higher in dialysis patients than non-dialysis patients, (88 vs. 76%, p < 0.001). Dialysis was associated with a 3% increase in risk of admission and 3 times the odds of in-hospital mortality (adjusted OR 3, 95% CI 2.7–2.3.3). Conclusions: ED visits for ADEs are substantially higher in dialysis patients than non-dialysis patients. In dialysis patients, ADEs associated with agents primarily affecting blood constituents are on the rise. ED visits for ADEs in dialysis patients have higher inpatient admissions and in-hospital mortality. Further studies are needed to identify and implement measures aimed at reducing ADEs in dialysis patients.