Prediction of Critical Illness During Out-of-Hospital Emergency Care

CONTEXT: Early identification of nontrauma patients in need of critical care services in the emergency setting may improve triage decisions and facilitate regionalization of critical care. OBJECTIVES: To determine the out-of-hospital clinical predictors of critical illness and to characterize the performance of a simple score for out-of-hospital prediction of development of critical illness during hospitalization. DESIGN AND SETTING: Population-based cohort study of an emergency medical services (EMS) system in greater King County, Washington (excluding metropolitan Seattle), that transports to 16 receiving facilities. PATIENTS: Nontrauma, non-cardiac arrest adult patients transported to a hospital by King County EMS from 2002 through 2006. Eligible records with complete data (N = 144,913) were linked to hospital discharge data and randomly split into development (n = 87,266 [60%]) and validation (n = 57,647 [40%]) cohorts. MAIN OUTCOME MEASURE: Development of critical illness, defined as severe sepsis, delivery of mechanical ventilation, or death during hospitalization. RESULTS: Critical illness occurred during hospitalization in 5% of the development (n = 4835) and validation (n = 3121) cohorts. Multivariable predictors of critical illness included older age, lower systolic blood pressure, abnormal respiratory rate, lower Glasgow Coma Scale score, lower pulse oximetry, and nursing home residence during out-of-hospital care (P < .01 for all). When applying a summary critical illness prediction score to the validation cohort (range, 0-8), the area under the receiver operating characteristic curve was 0.77 (95% confidence interval [CI], 0.76-0.78), with satisfactory calibration slope (1.0). Using a score threshold of 4 or higher, sensitivity was 0.22 (95% CI, 0.20-0.23), specificity was 0.98 (95% CI, 0.98-0.98), positive likelihood ratio was 9.8 (95% CI, 8.9-10.6), and negative likelihood ratio was 0.80 (95% CI, 0.79- 0.82). A threshold of 1 or greater for critical illness improved sensitivity (0.98; 95% CI, 0.97-0.98) but reduced specificity (0.17; 95% CI, 0.17-0.17). CONCLUSIONS: In a population-based cohort, the score on a prediction rule using out-of-hospital factors was significantly associated with the development of critical illness during hospitalization. This score requires external validation in an independent populationPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85143/1/Seymour - JAMA-2010-747-54.pdf11

Long-term Acute Care Hospital Utilization After Critical Illness

Long-term acute care hospitals have emerged as a novel approach for the care of patients recovering from severe acute illness, but the extent and growth of their activity at the national level is unknown

Conformal surface embeddings and extremal length

Given two Riemann surfaces with boundary and a homotopy class of topological embeddings between them, there is a conformal embedding in the homotopy class if and only if the extremal length of every simple multi-curve is decreased under the embedding. Furthermore, the homotopy class has a conformal embedding that misses an open disk if and only if extremal lengths are decreased by a definite ratio. This ratio remains bounded away from one under covers.Comment: 32 pages, 6 figures; v3: New Section 3.4, improved Example 4.4, other improvements throughou

Working With Capacity limitations: Operations Management in Critical Care

As your hospital\u27s ICU director, you are approached by the hospital\u27s administration to help solve ongoing problems with ICU bed availability. The ICU seems to be constantly full, and trauma patients in the emergency department sometimes wait up to 24 hours before receiving a bed. Additionally, the cardiac surgeons were forced to cancel several elective coronary-artery bypass graft cases because there was not a bed available for postoperative recovery. The hospital administrators ask whether you can decrease your ICU length of stay, and wonder whether they should expand the ICU to include more beds For help in understanding and optimizing your ICU\u27s throughput, you seek out the operations management researchers at your university

Accuracy of the discharge destination field in administrative data for identifying transfer to a long-term acute care hospital

<p>Abstract</p> <p>Background</p> <p>Long-term acute care hospitals (LTACs) provide specialized care for patients recovering from severe acute illness. In order to facilitate research into LTAC utilization and outcomes, we studied whether or not the discharge destination field in administrative data accurately identifies patients transferred to an LTAC following acute care hospitalization.</p> <p>Findings</p> <p>We used the 2006 hospitalization claims for United States Medicare beneficiaries to examine the performance characteristics of the discharge destination field in the administrative record, compared to the reference standard of directly observing LTAC transfers in the claims. We found that the discharge destination field was highly specific (99.7%, 95 percent CI: 99.7% - 99.8%) but modestly sensitive (77.3%, 95 percent CI: 77.0% - 77.6%), with corresponding low positive predictive value (72.6%, 95 percent CI: 72.3% - 72.9%) and high negative predictive value (99.8%, 95 percent CI: 99.8% - 99.8%). Sensitivity and specificity were similar when limiting the analysis to only intensive care unit patients and mechanically ventilated patients, two groups with higher rates of LTAC utilization. Performance characteristics were slightly better when limiting the analysis to Pennsylvania, a state with relatively high LTAC penetration.</p> <p>Conclusions</p> <p>The discharge destination field in administrative data can result in misclassification when used to identify patients transferred to long-term acute care hospitals. Directly observing transfers in the claims is the preferable method, although this approach is only feasible in identified data.</p

Volume, outcome, and the organization of intensive care

Increasing evidence suggests that high case volume is associated with improved outcomes in the intensive care unit (ICU). Potential explanations for the volume–outcome relationship include selective referral, clinical experience and organizational factors common to high-volume ICUs. Distinguishing between these explanations has important health policy implications, because outcomes at low-volume ICUs could be improved either by exporting best practices found at high-volume centers or by regionalizing adult critical care – two very different care strategies. Future research efforts should be directed at better characterizing the process of care in high-volume ICUs and exploring the feasibility of creating a regionalized system of care

Increased ICU workload is not associated with increased inpatient mortality

Poster Presented at ATS 2008 in Toronto, OntarioRationale: Although ICUs with higher overall patient volume may achieve better outcomes, there are few data on the effects of increasing patient loads on patients within the ICU. Methods: We examined 198,877 patients in 108 ICUs in 2002 - 2005 using conditional logistic regression with an ICU-specific fixed effect Main Results: Patients admitted on high census days had the same odds of inpatient mortality or transfer to another hospital as patients admitted on average or on low census days. Conclusions: The ICUs in this data set are able to function as high-reliability organizations.http://deepblue.lib.umich.edu/bitstream/2027.42/61402/1/ATS08_occupancy_poster_v02.pd

Breakdown in the Organ Donation Process and Its Effect on Organ Availability

Background. This study examines the effect of breakdown in the organ donation process on the availability of transplantable organs. A process breakdown is defined as a deviation from the organ donation protocol that may jeopardize organ recovery. Methods. A retrospective analysis of donation-eligible decedents was conducted using data from an independent organ procurement organization. Adjusted effect of process breakdown on organs transplanted from an eligible decedent was examined using multivariable zero-inflated Poisson regression. Results. An eligible decedent is four times more likely to become an organ donor when there is no process breakdown (adjusted OR: 4.01; 95% CI: 1.6838, 9.6414; &lt; 0.01) even after controlling for the decedent&apos;s age, gender, race, and whether or not a decedent had joined the state donor registry. However once the eligible decedent becomes a donor, whether or not there was a process breakdown does not affect the number of transplantable organs yielded. Overall, for every process breakdown occurring in the care of an eligible decedent, one less organ is available for transplant. Decedent&apos;s age is a strong predictor of likelihood of donation and the number of organs transplanted from a donor. Conclusion. Eliminating breakdowns in the donation process can potentially increase the number of organs available for transplant but some organs will still be lost

Breakdown in the Organ Donation Process and Its Effect on Organ Availability

Background. This study examines the effect of breakdown in the organ donation process on the availability of transplantable organs. A process breakdown is defined as a deviation from the organ donation protocol that may jeopardize organ recovery. Methods. A retrospective analysis of donation-eligible decedents was conducted using data from an independent organ procurement organization. Adjusted effect of process breakdown on organs transplanted from an eligible decedent was examined using multivariable zero-inflated Poisson regression. Results. An eligible decedent is four times more likely to become an organ donor when there is no process breakdown (adjusted OR: 4.01; 95% CI: 1.6838, 9.6414; P<0.01) even after controlling for the decedent’s age, gender, race, and whether or not a decedent had joined the state donor registry. However once the eligible decedent becomes a donor, whether or not there was a process breakdown does not affect the number of transplantable organs yielded. Overall, for every process breakdown occurring in the care of an eligible decedent, one less organ is available for transplant. Decedent’s age is a strong predictor of likelihood of donation and the number of organs transplanted from a donor. Conclusion. Eliminating breakdowns in the donation process can potentially increase the number of organs available for transplant but some organs will still be lost