Effect of concurrent oxygen therapy on accuracy of forecasting imminent postoperative desaturation
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| 024 | 7 | 0 | |a 10.1007/s10877-014-9629-8 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10877-014-9629-8 | ||
| 245 | 0 | 0 | |a Effect of concurrent oxygen therapy on accuracy of forecasting imminent postoperative desaturation |h [Elektronische Daten] |c [Hisham ElMoaqet, Dawn Tilbury, Satya Ramachandran] |
| 520 | 3 | |a Episodic postoperative desaturation occurs predominantly from respiratory depression or airway obstruction. Monitor display of desaturation is typically delayed by over 30s after these dynamic inciting events, due to perfusion delays, signal capture and averaging. Prediction of imminent critical desaturation could aid development of dynamic high-fidelity response systems that reduce or prevent the inciting event from occurring. Oxygen therapy is known to influence the depth and duration of desaturation epochs, thereby potentially influencing the accuracy of forecasting of desaturation. In this study, postoperative pulse oximetry data were retrospectively modeled using autoregressive methods to create prediction models for $${\hbox {SpO}}_2$$ SpO 2 and imminent critical desaturation in the postoperative period. The accuracy of these models in predicting near future $${\hbox {SpO}}_2$$ SpO 2 values was tested using root mean square error. The model accuracy for prediction of critical desaturation ( $${\hbox {SpO}}_2$$ SpO 2 $$\le 89\,\%$$ ≤ 89 % ) was evaluated using meta-analytical methods (sensitivity, specificity, likelihood ratios, diagnostic odds ratios and area under summary receiver operating characteristic curves). Between-study heterogeneity was used as a measure of reliability of the model across different patients and evaluated using the tau-squared statistic. Model performance was evaluated in $$20$$ 20 patients who received postoperative oxygen supplementation and $$20$$ 20 patients who did not receive oxygen. Our results show that model accuracy was high with root mean square errors between 0.2 and 2.8%. Prediction accuracy as defined by area under the curve for critical desaturation events was observed to be greater in patients receiving oxygen in the 60-s horizon ( $$0.95\pm 0.04$$ 0.95 ± 0.04 vs. $$0.76\pm 0.16$$ 0.76 ± 0.16 ). This was likely related to the higher frequency of events in this group (median [IQR] $$133.0$$ 133.0 $$[31.5, 508.2]$$ [ 31.5 , 508.2 ] ) than patients who were not treated with oxygen ( $$0$$ 0 $$[0,110]$$ [ 0 , 110 ] ; $$p<0.001$$ p < 0.001 ). Model reliability was reflected by the homogeneity of the prediction models which were homogenous across both prediction horizons and oxygen treatment groups. In conclusion, we report the use of autoregressive models to predict $${\hbox {SpO}}_2$$ SpO 2 and forecast imminent critical desaturation events in the postoperative period with high degree of accuracy. These models reliably predict critical desaturation in patients receiving supplemental oxygen therapy. While high-fidelity prophylactic interventions that could modify these inciting events are in development, our current study offers proof of concept that the afferent limb of such a system can be modeled with a high degree of accuracy. | |
| 540 | |a Springer Science+Business Media New York, 2014 | ||
| 690 | 7 | |a Pulse oximetry |2 nationallicence | |
| 690 | 7 | |a Oxygen therapy |2 nationallicence | |
| 690 | 7 | |a Time series modeling and prediction |2 nationallicence | |
| 690 | 7 | |a Autoregressive models |2 nationallicence | |
| 690 | 7 | |a Prediction evaluation metrics |2 nationallicence | |
| 700 | 1 | |a ElMoaqet |D Hisham |u Mechanical Engineering Department, University of Michigan, 48109, Ann Arbor, MI, USA |4 aut | |
| 700 | 1 | |a Tilbury |D Dawn |u Mechanical Engineering Department, University of Michigan, 48109, Ann Arbor, MI, USA |4 aut | |
| 700 | 1 | |a Ramachandran |D Satya |u Department of Anesthesiology, Medical School, University of Michigan, 48109, Ann Arbor, MI, USA |4 aut | |
| 773 | 0 | |t Journal of Clinical Monitoring and Computing |d Springer Netherlands |g 29/4(2015-08-01), 521-531 |x 1387-1307 |q 29:4<521 |1 2015 |2 29 |o 10877 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10877-014-9629-8 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10877-014-9629-8 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a ElMoaqet |D Hisham |u Mechanical Engineering Department, University of Michigan, 48109, Ann Arbor, MI, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Tilbury |D Dawn |u Mechanical Engineering Department, University of Michigan, 48109, Ann Arbor, MI, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Ramachandran |D Satya |u Department of Anesthesiology, Medical School, University of Michigan, 48109, Ann Arbor, MI, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Journal of Clinical Monitoring and Computing |d Springer Netherlands |g 29/4(2015-08-01), 521-531 |x 1387-1307 |q 29:4<521 |1 2015 |2 29 |o 10877 | ||