Background Hemodialysis (HD) therapy is an indispensable tool used in critical care management. Patients undergoing HD are at risk for intradialytic adverse events, ranging from muscle cramps to cardiac arrest. So far, there is no effective HD device–integrated algorithm to assist medical staff in response to these adverse events a step earlier during HD. Objective We aimed to develop machine learning algorithms to predict intradialytic adverse events in an unbiased manner. Methods Three-month dialysis and physiological time-series data were collected from all patients who underwent maintenance HD therapy at a tertiary care referral center. Dialysis data were collected automatically by HD devices, and physiological data were recorded by medical staff. Intradialytic adverse events were documented by medical staff according to patient complaints. Features extracted from the time series data sets by linear and differential analyses were used for machine learning to predict adverse events during HD. Results Time series dialysis data were collected during the 4-hour HD session in 108 patients who underwent maintenance HD therapy. There were a total of 4221 HD sessions, 406 of which involved at least one intradialytic adverse event. Models were built by classification algorithms and evaluated by four-fold cross-validation. The developed algorithm predicted overall intradialytic adverse events, with an area under the curve (AUC) of 0.83, sensitivity of 0.53, and specificity of 0.96. The algorithm also predicted muscle cramps, with an AUC of 0.85, and blood pressure elevation, with an AUC of 0.93. In addition, the model built based on ultrafiltration-unrelated features predicted all types of adverse events, with an AUC of 0.81, indicating that ultrafiltration-unrelated factors also contribute to the onset of adverse events. Conclusions Our results demonstrated that algorithms combining linear and differential analyses with two-class classification machine learning can predict intradialytic adverse events in quasi-real time with high AUCs. Such a methodology implemented with local cloud computation and real-time optimization by personalized HD data could warn clinicians to take timely actions in advance.
Approximately 500,000 dialysis patients in America are at a high risk of hyperkalemia, a condition where blood potassium becomes elevated above normal levels. Hyperkalemia is extremely dangerous, as it can result in severe cardiac complications if untreated. Hyperkalemia may be silent or present vague symptoms until those complications develop, at which point patients require emergency medical care. However, if patients have the ability to measure their potassium levels at home, they could detect hyperkalemia before it reaches a dangerous stage, and seek preventative medical care to avoid severe complications. Therefore, we have designed a novel device allowing patients to measure their blood potassium levels at home. The workflow of our solution is as follows: (1) patients obtain a blood sample from a finger prick, (2) potassium concentration is measured with an ion specific electrode (ISE), and (3) the device displays their potassium levels and a recommended course of action based on their hyperkalemic risk. We validate our solution with three major tests. First, our portable ISE technology must accurately measure potassium concentration in blood samples. Second, appropriate lancet parameters (gauge and depth) to minimize hemolysis in capillary blood samples must be found to minimize falsely elevated readings. Third, device portability and ease of use must be evaluated using patient input, as these factors will affect patient compliance. We have validated the use of portable ISE technology to feasibly measure potassium, and we continue to collect data for our second and third tests.
BACKGROUND Hemodialysis (HD) therapy is an indispensable tool used in critical care management. Patients undergoing HD are at risk for intradialytic adverse events, ranging from muscle cramps to cardiac arrest. So far, there is no effective HD device–integrated algorithm to assist medical staff in response to these adverse events a step earlier during HD. OBJECTIVE We aimed to develop machine learning algorithms to predict intradialytic adverse events in an unbiased manner. METHODS Three-month dialysis and physiological time-series data were collected from all patients who underwent maintenance HD therapy at a tertiary care referral center. Dialysis data were collected automatically by HD devices, and physiological data were recorded by medical staff. Intradialytic adverse events were documented by medical staff according to patient complaints. Features extracted from the time series data sets by linear and differential analyses were used for machine learning to predict adverse events during HD. RESULTS Time series dialysis data were collected during the 4-hour HD session in 108 patients who underwent maintenance HD therapy. There were a total of 4221 HD sessions, 406 of which involved at least one intradialytic adverse event. Models were built by classification algorithms and evaluated by four-fold cross-validation. The developed algorithm predicted overall intradialytic adverse events, with an area under the curve (AUC) of 0.83, sensitivity of 0.53, and specificity of 0.96. The algorithm also predicted muscle cramps, with an AUC of 0.85, and blood pressure elevation, with an AUC of 0.93. In addition, the model built based on ultrafiltration-unrelated features predicted all types of adverse events, with an AUC of 0.81, indicating that ultrafiltration-unrelated factors also contribute to the onset of adverse events. CONCLUSIONS Our results demonstrated that algorithms combining linear and differential analyses with two-class classification machine learning can predict intradialytic adverse events in quasi-real time with high AUCs. Such a methodology implemented with local cloud computation and real-time optimization by personalized HD data could warn clinicians to take timely actions in advance.
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