Table 1.

Summary of literature algorithms for basal insulin attenuation or suspension proposed by academic research groups

Reference paper	Type	Inputs	Prediction method	PH, min	T, mg/dl	Min–max suspension time, min	Assessment type
Buckingham et al. Diabetes Technol Ther, 2009 [14]	Prediction-based suspension	CGM	Simple linear regression in time, statistical models [15]	30	80	90–90	Inpatient [14]
Buckingham et al. Diabetes Care, 2010 [16]	Prediction-based suspension	CGM	Voting schema of 5 separate prediction algorithms [17]	35	80	30–90	Inpatient [16]
Hughes et al. J Diabetes Sci Technol, 2010 [22]	Detection-based attenuation	CGM	–	–	120	–	In silico [22]
Hughes et al. J Diabetes Sci Technol, 2010 [22]	Prediction-based attenuation	CGM, insulin	Kalman filter with metabolic state observer	15	120	–	In silico [22]
Patek et al. IEEE Trans Biomed Eng, 2012 [26]	Prediction-based attenuation	CGM, insulin	Simple linear regression in time	17	112.5	–	In silico [26]
Cameron et al. J Diabetes Sci Technol, 2012 [19]	Prediction-based suspension	CGM	Kalman filter [18]	70	80	0-120	Inpatient [19], outpatient [20]
Buckingham et al. Diabetes Technol Ther, 2013 [20]	Prediction-based suspension	CGM	Kalman filter [18]	30	80	0–120	Outpatient [20, 38, 39, 46]
Hughes et al. Comput Methods Programs Biomed, 2013 [25]	Prediction-based attenuation	CGM, insulin	Simple linear regression in glucose with parameters from historical data	30	120 or 140	–	In silico [25]
Stenerson et al. J Diabetes Sci Technol, 2014 [21]	Prediction-based suspension	CGM, accelerometer, heart rate	Kalman filter [18]	30	80	0–120	In silico [21]