Background An integrated kidney disease healthcare company implemented a peritoneal dialysis (PD) remote treatment monitoring (RTM) application in 2016. We assessed if RTM utilization associates with hospitalization and technique failure rates.Methods We used data from adult (age $18 years) patients on PD treated from October 2016 through May 2019 who registered online for the RTM. Patients were classified by RTM use during a 30-day baseline after registration. Groups were: nonusers (never entered data), moderate users (entered one to 15 treatments), and frequent users (entered .15 treatments). We compared hospital admission/day and sustained technique failure (required .6 consecutive weeks of hemodialysis) rates over 3, 6, 9, and 12 months of follow-up using Poisson and Cox models adjusted for patient/clinical characteristics.Results Among 6343 patients, 65% were nonusers, 11% were moderate users, and 25% were frequent users. Incidence rate of hospital admission was 22% (incidence rate ratio [IRR]50.78; P50.002), 24% (IRR50.76; P,0.001), 23% (IRR50.77; P#0.001), and 26% (IRR50.74; P#0.001) lower in frequent users after 3, 6, 9, and 12 months, respectively, versus nonusers. Incidence rate of hospital days was 38% (IRR50.62; P50.013), 35% (IRR50.65; P50.001), 34% (IRR50.66; P#0.001), and 32% (IRR50.68; P,0.001) lower in frequent users after 3, 6, 9, and 12 months, respectively, versus nonusers. Sustained technique failure risk at 3, 6, 9, and 12 months was 33% (hazard ratio [HR]50.67; P50.020), 31% (HR50.69; P50.003), 31% (HR50.69; P50.001), and 27% (HR50.73; P50.001) lower, respectively, in frequent users versus nonusers. Among a subgroup of survivors of the 12-month follow-up, sustained technique failure risk was 26% (HR50.74; P50.023) and 21% (HR50.79; P50.054) lower after 9 and 12 months, respectively, in frequent users versus nonusers.Conclusions Our findings suggest frequent use of an RTM application associates with less hospital admissions, shorter hospital length of stay, and lower technique failure rates. Adoption of RTM applications may have the potential to improve timely identification/intervention of complications.
Artificial intelligence (AI) is considered as the next natural progression of traditional statistical techniques. Advances in analytical methods and infrastructure enable AI to be applied in health care. While AI applications are relatively common in fields like ophthalmology and cardiology, its use is scarcely reported in nephrology. We present the current status of AI in research toward kidney disease and discuss future pathways for AI. The clinical applications of AI in progression to end‐stage kidney disease and dialysis can be broadly subdivided into three main topics: (a) predicting events in the future such as mortality and hospitalization; (b) providing treatment and decision aids such as automating drug prescription; and (c) identifying patterns such as phenotypical clusters and arteriovenous fistula aneurysm. At present, the use of prediction models in treating patients with kidney disease is still in its infancy and further evidence is needed to identify its relative value. Policies and regulations need to be addressed before implementing AI solutions at the point of care in clinics. AI is not anticipated to replace the nephrologists’ medical decision‐making, but instead assist them in providing optimal personalized care for their patients.
Digitization of healthcare will be a major innovation driver in the coming decade. Also, enabled by technological advancements and electronics miniaturization, wearable health device (WHD) applications are expected to grow exponentially. This, in turn, may make 4P medicine (predictive, precise, preventive and personalized) a more attainable goal within dialysis patient care. This article discusses different use cases where WHD could be of relevance for dialysis patient care, i.e. measurement of heart rate, arrhythmia detection, blood pressure, hyperkalaemia, fluid overload and physical activity. After adequate validation of the different WHD in this specific population, data obtained from WHD could form part of a body area network (BAN), which could serve different purposes such as feedback on actionable parameters like physical inactivity, fluid overload, danger signalling or event prediction. For a BAN to become clinical reality, not only must technical issues, cybersecurity and data privacy be addressed, but also adequate models based on artificial intelligence and mathematical analysis need to be developed for signal optimization, data representation, data reliability labelling and interpretation. Moreover, the potential of WHD and BAN can only be fulfilled if they are part of a transformative healthcare system with a shared responsibility between patients, healthcare providers and the payors, using a step-up approach that may include digital assistants and dedicated ‘digital clinics’. The coming decade will be critical in observing how these developments will impact and transform dialysis patient care and will undoubtedly ask for an increased ‘digital literacy’ for all those implicated in their care.
Background: We developed a machine learning (ML) model that predicts the risk of a hemodialysis (HD) patient having an undetected SARS-CoV-2 infection that is identified after the following 3 or more 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 a ML model (XGBoost) that uses 81 variables to predict the likelihood of an adult HD patient 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 HD patients to build the ML model (11,166 COVID-19 positive cases and 29,324 unaffected (control) patients). 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 an HD patient 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 HD patients at risk of having COVID-19 at least three days before there would be a clinical suspicion of the disease.
Background Inadequate refilling from extravascular compartments during hemodialysis can lead to intradialytic symptoms, such as hypotension, nausea, vomiting, and cramping/myalgia. Relative blood volume (RBV) plays an important role in adapting the ultrafiltration rate which in turn has a positive effect on intradialytic symptoms. It has been clinically challenging to identify changes RBV in real time to proactively intervene and reduce potential negative consequences of volume depletion. Leveraging advanced technologies to process large volumes of dialysis and machine data in real time and developing prediction models using machine learning (ML) is critical in identifying these signals. Method We conducted a proof-of-concept analysis to retrospectively assess near real-time dialysis treatment data from in-center patients in six clinics using Optical Sensing Device (OSD), during December 2018 to August 2019. The goal of this analysis was to use real-time OSD data to predict if a patient’s relative blood volume (RBV) decreases at a rate of at least − 6.5 % per hour within the next 15 min during a dialysis treatment, based on 10-second windows of data in the previous 15 min. A dashboard application was constructed to demonstrate how reporting structures may be developed to alert clinicians in real time of at-risk cases. Data was derived from three sources: (1) OSDs, (2) hemodialysis machines, and (3) patient electronic health records. Results Treatment data from 616 in-center dialysis patients in the six clinics was curated into a big data store and fed into a Machine Learning (ML) model developed and deployed within the cloud. The threshold for classifying observations as positive or negative was set at 0.08. Precision for the model at this threshold was 0.33 and recall was 0.94. The area under the receiver operating curve (AUROC) for the ML model was 0.89 using test data. Conclusions The findings from our proof-of concept analysis demonstrate the design of a cloud-based framework that can be used for making real-time predictions of events during dialysis treatments. Making real-time predictions has the potential to assist clinicians at the point of care during hemodialysis.
Background: We developed two unique machine learning (ML) models that predict risk of: 1) a major COVID-19 outbreak in the service county of a local HD population within following week, and 2) a hemodialysis (HD) patient having an undetected SARS-CoV-2 infection that is identified after following 3 or more days. Methods: We used county-level data from United States population (March 2020) and HD patient data from a network of clinics (February-May 2020) to develop two ML models. First was a county-level model that used data from general and HD populations (21 variables); outcome of a COVID-19 outbreak in a dialysis service area was defined as a clinic being located in one of the national counties with the highest growth in COVID-19 positive cases (number and people per million (ppm)) in general population during 22-28 Mar 2020. Second was a patient-level model that used HD patient data (82 variables) to predict an individual having an undetected SARS-CoV-2 infection that is identified in subsequent ≥3 days. Results: Among 1682 counties with dialysis clinics, 82 (4.9%) had a COVID-19 outbreak during 22-28 Mar 2020. Area under the receiver operating characteristic curve (AUROC) for the county-level model was 0.86 in testing dataset. Top predictor of a county experiencing an outbreak was the COVID-19 positive ppm in the general population in the prior week. In a select group (n=11,664) used to build the patient-level model, 28% of patients had COVID-19; prevalence was by design 10% in the testing dataset. AUROC for the patient-level model was 0.71 in the testing dataset. Top predictor of an HD patient having a SARS-CoV-2 infection was mean pre-HD body temperature in the prior week. Conclusions: Developed ML models appear suitable for predicting counties at risk of a COVID-19 outbreak and HD patients at risk of having an undetected SARS-CoV-2 infection.
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 RT-PCR test to investigate suspicion of a SARS-CoV-2 infection between 01 May and 01 Sep 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 non-survivor 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 (8,895 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, lymphocytes, albumin, and interdialytic weight gain (IDWG) between COVID-19 positive and negative patient. For COVID-19 positive group, we observed significantly different clinical trends (p<0.05) in pre-HD pulse rate, lymphocytes, albumin and neutrophil-lymphocyte ratio (NLR) between survivors and non-survivors. 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
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 À14 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.
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