Search

Background Preventing urgent intubation and upgrade in level of care in patients with subclinical deterioration could be of great utility in hospitalized patients. Early detection should result in decreased mortality, duration of stay, and/or resource use. The goal of this study was to externally validate a previously developed, vital sign-based, intensive care unit, respiratory instability model on a separate population, intermediate care patients. Methods From May 2014 to May 2016, the model calculated relative risk of adverse events every 15 minutes ( n = 373,271 observations) for 2,050 patients in a surgical intermediate care unit. Results We identified 167 upgrades and 57 intubations. The performance of the model for predicting upgrades within 12 hours was highly significant with an area under the curve of 0.693 (95% confidence interval, 0.658–0.724). The model was well calibrated with relative risks in the highest and lowest deciles of 2.99 and 0.45, respectively (a 6.6-fold increase). The model was effective at predicting intubation, with a demonstrated area under the curve within 12 hours of the event of 0.748 (95% confidence interval, 0.685–0.800). The highest and lowest deciles of observed relative risk were 3.91 and 0.39, respectively (a 10.1-fold increase). Univariate analysis of vital signs showed that transfer upgrades were associated, in order of importance, with rising respiration rate, rising heart rate, and falling pulse-oxygen saturation level. Conclusion The respiratory instability model developed previously is valid in intermediate care patients to predict both urgent intubations and requirements for upgrade in level of care to an intensive care unit.

Background Continuous predictive monitoring has been employed successfully to predict subclinical adverse events. Should low values on these models, however, reassure us that a patient will not have an adverse outcome? Negative predictive values of such models could help predict safe patient discharge. The goal of this study was to validate the negative predictive value of an ensemble model for critical illness (using previously developed models for respiratory instability, hemorrhage, and sepsis) based on bedside monitoring data in the intensive care units and intermediate care unit. Methods We calculated the relative risk of 3 critical illnesses for all patients every 15 minutes (n = 124,588) for 2,924 patients downgraded from the surgical intensive care units and intermediate care unit between May 2014 to May 2016. We constructed an ensemble model to estimate at the time of intensive care units or intermediate care unit discharge the probability of favorable outcome after downgrade. Results Outputs form the ensemble model stratified patients by risk of favorable and bad outcomes in both intensive care units/intermediate care unit; area under the receiver operating characteristic curve = .639/.629 respectively for favorable outcomes and .645/.641 for adverse events. These performance characteristics are commensurate with published models for predicting readmission. The ensemble model remained a statistically significant predictor after adjusting for hospital duration of stay and admitting service. The rate of favorable outcome in the highest and lowest deciles in the intensive care units were 76.2% and 27.3% (2.8-fold decrease) and 88.3% and 33.2% in the intermediate care unit (2.7-fold decrease), respectively. Conclusion An ensemble model for critical illness predicts favorable outcome after downgrade and safe patient discharge (hospital stay

Background Analysis and modeling of data monitoring vital signs and waveforms in patients in a surgical/trauma intensive care unit (STICU) may allow for early identification and treatment of patients with evolving respiratory failure. Methods Between February 2011 and March 2012, data of vital signs and waveforms for STICU patients were collected. Every-15-minute calculations (n = 172,326) of means and standard deviations of heart rate (HR), respiratory rate (RR), pulse-oxygen saturation (SpO2), cross-correlation coefficients, and cross-sample entropy for HR-RR, RR-SpO2, and HR-SpO2, and cardiorespiratory coupling were calculated. Urgent intubations were recorded. Univariate analyses were performed for the periods