To the Editor: 

We read with interest the review by Taenzer et al. ,1in which a conceptual framework for a successful patient surveillance system is clearly described.

We agree that overcoming the problem of nuisance alarms, which can be exacerbated by continuous surveillance, is important in all surveillance systems. The authors recommend that alarm thresholds based on the distributions of continuously acquired datasets of physiologic variables in representative general patient populations not in the intensive care unit are combined with notification delay to help achieve the required alarm accuracy rate of at least 90%.

The authors kindly include our work in their review.2Our alarm model was developed using continuously acquired data on representative general medical and surgical patient populations not in the intensive care unit, uses an alarm threshold based on the underlying distributions of five vital signs, requires an abnormality to persist for 4 min out of 5 before an alarm is generated (a form of notification delay), and had a “true alarm” rate of 94.5%, meeting many of the criteria described by Taenzer et al.  But our randomized controlled trial did not assess the effects of a multi-parameter patient status model on patient outcome, as they suggest. Rather, our trial compared mandated five-channel continuous monitoring with standard care, and showed that extra monitoring had no effect on adverse event rates or mortality. The performance of the multi-parameter patient status model (Biosign, now VisensiaTM; OBS Medical, Abingdon, Oxon, United Kingdom) was retrospectively assessed using the five-channel monitoring data. More recently the introduction of this model to a 24-bed step-down unit in Pittsburgh has been associated with reduced periods of cardiorespiratory instability in step-down unit patients.3 

From their own work, the authors suggest that heart rate and oxygen saturation distributions may vary little across patient groups. Our recently published distributions from 64,622 h of vital-sign data, acquired from 863 acutely ill in-hospital patients, add to this observation, showing largely similar distributions for systolic blood pressure and respiratory rate (as well as heart rate and oxygen saturation) in medical and surgical patients, studied both in the United Kingdom and the United States. As they suggest, we proposed alert thresholds based on these distributions.4 

The authors review their promising results from a before-and-after study using the Patient SafetyNetTMsystem (Masimo Corporation, Irvine, CA).5However, the alerting criterion for oxygen saturation of 80% used in the “after” phase in this study would alert for 0.67% of patient observations (as compared with 6.25% for an oxygen saturation of 90%) in populations with the distribution of oxygen saturations seen in our study.4Although this might explain the reduction in medical emergency team activations that occurred after the Patient SafetyNetTMsystem was introduced, we suggest it may also demonstrate the difficulties of highlighting deteriorating patients without creating an unmanageable number of “nuisance alarms” rightly discussed by the authors.

Taenzer AH, Pyke JB, McGrath SP: A review of current and emerging approaches to address failure-to-rescue. ANESTHESIOLOGY 2011; 115:421–31
Watkinson PJ, Barber VS, Price JD, Hann A, Tarassenko L, Young JD: A randomised controlled trial of the effect of continuous electronic physiological monitoring on the adverse event rate in high risk medical and surgical patients. Anaesthesia 2006; 61:1031–9
Hravnak M, Devita MA, Clontz A, Edwards L, Valenta C, Pinsky MR: Cardiorespiratory instability before and after implementing an integrated monitoring system. Crit Care Med 2011; 39:65–72
Tarassenko L, Clifton DA, Pinsky MR, Hravnak MT, Woods JR, Watkinson PJ: Centile-based early warning scores derived from statistical distributions of vital signs. Resuscitation 2011; 82:1013–8
Taenzer AH, Pyke JB, McGrath SP, Blike GT: Impact of pulse oximetry surveillance on rescue events and intensive care unit transfers: A before-and-after concurrence study. ANESTHESIOLOGY 2010; 112:282–7