Multi-infusion with integrated multiple pressure sensing allows earlier detection of line occlusions

Abstract

Background Occlusions of intravenous (IV) tubing can prevent vital and time-critical medication or solutions from being delivered into the bloodstream of patients receiving IV therapy. At low flow rates (≤ 1 ml/h) the alarm delay (time to an alert to the user) can be up to 2 h using conventional pressure threshold algorithms. In order to reduce alarm delays we developed and evaluated the performance of two new real-time occlusion detection algorithms and one co-occlusion detector that determines the correlation in trends in pressure changes for multiple pumps. Methods Bench-tested experimental runs were recorded in triplicate at rates of 1, 2, 4, 8, 16, and 32 ml/h. Each run consisted of 10 min of non-occluded infusion followed by a period of occluded infusion of 10 min or until a conventional occlusion alarm at 400 mmHg occurred. The first algorithm based on binary logistic regression attempts to detect occlusions based on the pump’s administration rate Q(t) and pressure sensor readings P(t). The second algorithm continuously monitored whether the actual variation in the pressure exceeded a threshold of 2 standard deviations (SD) above the baseline pressure. When a pump detected an occlusion using the SD algorithm, a third algorithm correlated the pressures of multiple pumps to detect the presence of a shared occlusion. The algorithms were evaluated using 6 bench-tested baseline single-pump occlusion scenarios, 9 single-pump validation scenarios and 7 multi-pump co-occlusion scenarios (i.e. with flow rates of 1 + 1, 1 + 2, 1 + 4, 1 + 8, 1 + 16, and 1 + 32 ml/h respectively). Alarm delay was the primary performance measure. Results In the baseline single-pump occlusion scenarios, the overall mean ± SD alarm delay of the regression and SD algorithms were 1.8 ± 0.8 min and 0.4 ± 0.2 min, respectively. Compared to the delay of the conventional alarm this corresponds to a mean time reduction of 76% (P = 0.003) and 95% (P = 0.001), respectively. In the validation scenarios the overall mean ± SD alarm delay of the regression and SD algorithms were respectively 1.8 ± 1.6 min and 0.3 ± 0.2 min, corresponding to a mean time reduction of 77% and 95%. In the multi-pump scenarios a correlation > 0.8 between multiple pump pressures after initial occlusion detection by the SD algorithm had a mean ± SD alarm delay of 0.4 ± 0.2 min. In 2 out of the 9 validation scenarios an occlusion was not detected by the regression algorithm before a conventional occlusion alarm occurred. Otherwise no occlusions were missed. Conclusions In single pumps, both the regression and SD algorithm considerably reduced alarm delay compared to conventional pressure limit-based detection. The SD algorithm appeared to be more robust than the regression algorithm. For multiple pumps the correlation algorithm reliably detected co-occlusions. The latter may be used to localize the segment of tubing in which the occlusion occurs. Trial registration Not applicable.

Performance evaluation study of a commercially available smart patient-controlled analgesia pump with the microbalance method and an infusion analyzer

“…Occlusion is a blockage or closing of the infusion flow passage, usually referring to an interruption or impediment of an infusion tube due to a blockage, momentary closure, or obstruction of the passageway or blood vessel. Occlusion in the fluid tube of infusion pumps can prevent vital and time-critical medication from being delivered into the bloodstream of patients, resulting in patient harm caused by cessation of therapy [ 40 41 ]. Therefore, it is very important to immediately alert the patient and medical care providers to an occlusion event.…”

“…The measured alarm delay times were consistent with other studies, which showed that the mean time to trigger the occlusion alarm was significantly longer at low infusion rates [ 42 ]. The alarm delays of PS-1000 meet the overall time delay criteria of the conventional alarm threshold of the occlusion pressure (300–800 mmHg) [ 41 ]. This is consistent with other reports [ 41 42 ].…”

“…The alarm delays of PS-1000 meet the overall time delay criteria of the conventional alarm threshold of the occlusion pressure (300–800 mmHg) [ 41 ]. This is consistent with other reports [ 41 42 ].…”

Development of smart infusion pumps: State of the art and future perspectives

“…At the same time, most devices are also equipped with IP door open alarms, infusion completion alarms, operation forgetting alarms, and low battery alarms, etc. Pressure sensing technology can be used to detect blockages in infusion tubes 37 . Most bubble alarms use complex ultrasound or optical detection methods, which can detect bubbles as small as 0.1 mL of volume 10 .…”

Exploration of an Alarm Sensor to Detect Infusion Failure Administered by Syringe Pumps

“…Another approach is a drip bag scale, which can deduce the flow rate due to the reduced weight of the infusion bag [27]. Additionally, there are pressure sensing techniques to detect occlusions in infusion tubing [28]. Intelligent infusion pumps and the methods presented can reduce the number of medication errors, but not completely eliminate them [24].…”