Predicting SARS-CoV-2 infection among hemodialysis patients using multimodal data.

Background: The coronavirus disease 2019 (COVID-19) pandemic has created more devastation among dialysis patients than among the general population. Patient-level prediction models for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for the early identification of patients to prevent and mitigate outbreaks within dialysis clinics. As the COVID-19 pandemic evolves, it is unclear whether or not previously built prediction models are still sufficiently effective. Methods: We developed a machine learning (XGBoost) model to predict during the incubation period a SARS-CoV-2 infection that is subsequently diagnosed after 3 or more days. We used data from multiple sources, including demographic, clinical, treatment, laboratory, and vaccination information from a national network of hemodialysis clinics, socioeconomic information from the Census Bureau, and county-level COVID-19 infection and mortality information from state and local health agencies. We created prediction models and evaluated their performances on a rolling basis to investigate the evolution of prediction power and risk factors. Result: From April 2020 to August 2020, our machine learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.75, an improvement of over 0.07 from a previously developed machine learning model published by Kidney360 in 2021. As the pandemic evolved, the prediction performance deteriorated and fluctuated more, with the lowest AUROC of 0.6 in December 2021 and January 2022. Over the whole study period, that is, from April 2020 to February 2022, fixing the false-positive rate at 20%, our model was able to detect 40% of the positive patients. We found that features derived from local infection information reported by the Centers for Disease Control and Prevention (CDC) were the most important predictors, and vaccination status was a useful predictor as well. Whether or not a patient lives in a nursing home was an effective predictor before vaccination, but became less predictive after vaccination. Conclusion: As found in our study, the dynamics of the prediction model are frequently changing as the pandemic evolves. County-level infection information and vaccination information are crucial for the success of early COVID-19 prediction models. Our results show that the proposed model can effectively identify SARS-CoV-2 infections during the incubation period. Prospective studies are warranted to explore the application of such prediction models in daily clinical practice.

Inclement Weather and Risk of Missing Scheduled Hemodialysis Appointments among Patients with Kidney Failure.

BACKGROUND: Nonadherence to hemodialysis appointments could potentially result in health complications that can influence morbidity and mortality. We examined the association between different types of inclement weather and hemodialysis appointment adherence. METHODS: We analyzed health records of 60,135 patients with kidney failure who received in-center hemodialysis treatment at Fresenius Kidney Care clinics across the Northeastern US counties during 2001-2019. County-level daily meteorological data on rainfall, hurricane and tropical storm events, snowfall, snow depth, and wind speed were extracted using National Oceanic and Atmosphere Agency data sources. A time-stratified case-crossover study design with conditional Poisson regression was used to estimate the effect of inclement weather exposures within the Northeastern US region. We applied a distributed lag nonlinear model framework to evaluate the delayed effect of inclement weather for up to 1 week. RESULTS: We observed positive associations between inclement weather and missed appointment (rainfall, hurricane and tropical storm, snowfall, snow depth, and wind advisory) when compared with noninclement weather days. The risk of missed appointments was most pronounced during the day of inclement weather (lag 0) for rainfall (incidence rate ratio [RR], 1.03 per 10-mm rainfall; 95% confidence interval [CI], 1.02 to 1.03) and snowfall (RR, 1.02; 95% CI, 1.01 to 1.02). Over 7 days (lag 0-6), hurricane and tropical storm exposures were associated with a 55% higher risk of missed appointments (RR, 1.55; 95% CI, 1.22 to 1.98). Similarly, 7-day cumulative exposure to sustained wind advisories was associated with 29% higher risk (RR, 1.29; 95% CI, 1.25 to 1.31), while wind gusts advisories showed a 34% higher risk (RR, 1.34; 95% CI, 1.29 to 1.39) of missed appointment. CONCLUSIONS: Inclement weather was associated with higher risk of missed hemodialysis appointments within the Northeastern United States. Furthermore, the association between inclement weather and missed hemodialysis appointments persisted for several days, depending on the inclement weather type.

Assessing the impact of transitional care units on dialysis patient outcomes: A multicenter, propensity score-matched analysis.

INTRODUCTION: Inadequate predialysis care and education impacts the selection of a dialysis modality and is associated with adverse clinical outcomes. Transitional care units (TCUs) aim to meet the unmet educational needs of incident dialysis patients, but their impact beyond increasing home dialysis utilization has been incompletely characterized. METHODS: This retrospective study included adults initiating in-center hemodialysis at a TCU, matched to controls (1:4) with no TCU history initiating in-center hemodialysis. Patients were followed for up to 14 months. TCUs are dedicated spaces where staff provide personalized education and as-needed adjustments to dialysis prescriptions. For many patients, therapy was initiated with four to five weekly dialysis sessions, with at least some sessions delivered by home dialysis machines. Outcomes included survival, first hospitalization, transplant waiting-list status, post-TCU dialysis modality, and vascular access type. FINDINGS: The study included 724 patients initiating dialysis across 48 TCUs, with 2892 well-matched controls. At the end of 14 months, patients initiating dialysis in a TCU were significantly more likely to be referred and/or wait-listed for a kidney transplant than controls (57% vs. 42%; p < 0.0001). Initiation of dialysis at a TCU was also associated with significantly lower rates of receiving in-center hemodialysis at 14 months (74% vs. 90%; p < 0.0001) and higher rates of arteriovenous access (70% vs. 63%; p = 0.003). Although not statistically significant, TCU patients were more likely to survive and less likely to be hospitalized during follow-up than controls. DISCUSSION: Although TCUs are sometimes viewed as only a means for enhancing utilization of home dialysis, patients attending TCUs exhibited more favorable outcomes across all endpoints. In addition to being 2.5-fold more likely to receive home dialysis, TCU patients were 42% more likely to be referred for transplantation. Our results support expanding utilization of TCUs for patients with inadequate predialysis support.

Trajectories of clinical and laboratory characteristics associated with COVID-19 in hemodialysis patients by survival.

INTRODUCTION: The clinical impact of COVID-19 has not been established in the dialysis population. We evaluated the trajectories of clinical and laboratory parameters in hemodialysis (HD) patients. METHODS: We used data from adult HD patients treated at an integrated kidney disease company who received a reverse transcription polymerase chain reaction (RT-PCR) test to investigate suspicion of a severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection between May 1 and September 1, 2020. Nonparametric smoothing splines were used to fit data for individual trajectories and estimate the mean change over time in patients testing positive or negative for SARS-CoV-2 and those who survived or died within 30 days of first suspicion or positive test date. For each clinical parameter of interest, the difference in average daily changes between COVID-19 positive versus negative group and COVID-19 survivor versus nonsurvivor group was estimated by fitting a linear mixed effects model based on measurements in the 14 days before (i.e., Day -14 to Day 0) Day 0. RESULTS: There were 12,836 HD patients with a suspicion of COVID-19 who received RT-PCR testing (8895 SARS-CoV-2 positive). We observed significantly different trends (p < 0.05) in pre-HD systolic blood pressure (SBP), pre-HD pulse rate, body temperature, ferritin, neutrophils, lymphocytes, albumin, and interdialytic weight gain (IDWG) between COVID-19 positive and negative patients. For COVID-19 positive group, we observed significantly different clinical trends (p < 0.05) in pre-HD pulse rate, lymphocytes, neutrophils, and albumin between survivors and nonsurvivors. We also observed that, in the group of survivors, most clinical parameters returned to pre-COVID-19 levels within 60-90 days. CONCLUSION: We observed unique temporal trends in various clinical and laboratory parameters among HD patients who tested positive versus negative for SARS-CoV-2 infection and those who survived the infection versus those who died. These trends can help to define the physiological disturbances that characterize the onset and course of COVID-19 in HD patients.

SARS-CoV-2 in Spent Dialysate from Chronic Peritoneal Dialysis Patients with COVID-19.

Background: To date, it is unclear whether SARS-CoV-2 is present in spent dialysate from patients with COVID-19 on peritoneal dialysis (PD). Our aim was to assess the presence or absence of SARS-CoV-2 in spent dialysate from patients on chronic PD who had a confirmed diagnosis of COVID-19. Methods: Spent PD dialysate samples from patients on PD who were positive for COVID-19 were collected between March and August 2020. The multiplexed, real-time RT-PCR assay contained primer/probe sets specific to different SARS-CoV-2 genomic regions and to bacteriophage MS2 as an internal process control for nucleic acid extraction. Demographic and clinical data were obtained from patients' electronic health records. Results: A total of 26 spent PD dialysate samples were collected from 11 patients from ten dialysis centers. Spent PD dialysate samples were collected, on average, 25±13 days (median, 20; range, 10-45) after the onset of symptoms. The temporal distance of PD effluent collection relative to the closest positive nasal-swab RT-PCR result was 15±11 days (median, 14; range, 1-41). All 26 PD effluent samples tested negative at three SARS-CoV-2 genomic regions. Conclusions: Our findings indicate the absence of SARS-CoV-2 in spent PD dialysate collected at ≥10 days after the onset of COVID-19 symptoms. We cannot rule out the presence of SARS-CoV-2 in spent PD dialysate in the early stage of COVID-19.

Chronic Prescription Opioid Use Before and After Total Hip and Knee Arthroplasty in Patients Younger Than 65 Years.

BACKGROUND: Opioids are commonly prescribed to patients with painful and symptomatic degenerative joint disease preoperatively as a nonoperative intervention to reduce patients' symptoms and pain. The goal of total joint arthroplasty (TJA) is to reduce or eliminate the painful symptoms of degenerative joint disease. Due to the addictive property of opioid medications, some patients may develop a pattern of chronic opioid use after TJA. METHODS: We used MarketScan Commercial Claims and Encounters database to identify 125,019 patients (age <65 years) who underwent total knee arthroplasty (TKA) and total hip arthroplasty (THA) between 2009 and 2012. During the study period, opioid use was analyzed 3 months before surgery and at 12 months after surgery. We defined chronic opioid use as having 2 or more opioid prescriptions filled within any 6-week period. Multivariate logistic regression was used. RESULTS: Of the 24,127 patients who were chronic prescription opioid users before surgery, 72% were no longer chronic users 1 year after surgery. Of the 100,892 patients who were nonusers before surgery, 4% became chronic users within 1 year after surgery. TKA and hospital stay longer than 3 days were significant risk factors of persisting chronic opioid use after surgery, while age played a mixed role in predicting change of opioid use. CONCLUSION: Using our definition of chronic use, overall chronic opioid use decreased from 19% to 9% after TJA. Patients were more likely to cease chronic opioid use after TJA (72%) than to become chronic users (4%).