Colin R. Cooke

Duration of resuscitation efforts and survival after in-hospital cardiac arrest: an observational study

BACKGROUND During in-hospital cardiac arrests, how long resuscitation attempts should be continued before termination of efforts is unknown. We investigated whether duration of resuscitation attempts varies between hospitals and whether patients at hospitals that attempt resuscitation for longer have higher survival rates than do those at hospitals with shorter durations of resuscitation efforts. METHODS Between 2000 and 2008, we identified 64,339 patients with cardiac arrests at 435 US hospitals within the Get With The Guidelines—Resuscitation registry. For each hospital, we calculated the median duration of resuscitation before termination of efforts in non-survivors as a measure of the hospitals overall tendency for longer attempts. We used multilevel regression models to assess the association between the length of resuscitation attempts and risk-adjusted survival. Our primary endpoints were immediate survival with return of spontaneous circulation during cardiac arrest and survival to hospital discharge. FINDINGS 31,198 of 64,339 (48·5%) patients achieved return of spontaneous circulation and 9912 (15·4%) survived to discharge. For patients achieving return of spontaneous circulation, the median duration of resuscitation was 12 min (IQR 6-21) compared with 20 min (14-30) for non-survivors. Compared with patients at hospitals in the quartile with the shortest median resuscitation attempts in non-survivors (16 min [IQR 15-17]), those at hospitals in the quartile with the longest attempts (25 min [25-28]) had a higher likelihood of return of spontaneous circulation (adjusted risk ratio 1·12, 95% CI 1·06-1·18; p<0·0001) and survival to discharge (1·12, 1·02-1·23; 0·021). INTERPRETATION Duration of resuscitation attempts varies between hospitals. Although we cannot define an optimum duration for resuscitation attempts on the basis of these observational data, our findings suggest that efforts to systematically increase the duration of resuscitation could improve survival in this high-risk population. FUNDING American Heart Association, Robert Wood Johnson Foundation Clinical Scholars Program, and the National Institutes of Health.

Population burden of long-term survivorship after severe sepsis in Older Americans

To ascertain the absolute number of Medicare beneficiaries surviving at least 3 years after severe sepsis and to estimate their burden of cognitive dysfunction and disability.

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 population.

Guidelines for Family-Centered Care in the Neonatal, Pediatric, and Adult ICU.

Objective: To provide clinicians with evidence-based strategies to optimize the support of the family of critically ill patients in the ICU. Methods: We used the Council of Medical Specialty Societies principles for the development of clinical guidelines as the framework for guideline development. We assembled an international multidisciplinary team of 29 members with expertise in guideline development, evidence analysis, and family-centered care to revise the 2007 Clinical Practice Guidelines for support of the family in the patient-centered ICU. We conducted a scoping review of qualitative research that explored family-centered care in the ICU. Thematic analyses were conducted to support Population, Intervention, Comparison, Outcome question development. Patients and families validated the importance of interventions and outcomes. We then conducted a systematic review using the Grading of Recommendations, Assessment, Development and Evaluations methodology to make recommendations for practice. Recommendations were subjected to electronic voting with pre-established voting thresholds. No industry funding was associated with the guideline development. Results: The scoping review yielded 683 qualitative studies; 228 were used for thematic analysis and Population, Intervention, Comparison, Outcome question development. The systematic review search yielded 4,158 reports after deduplication and 76 additional studies were added from alerts and hand searches; 238 studies met inclusion criteria. We made 23 recommendations from moderate, low, and very low level of evidence on the topics of: communication with family members, family presence, family support, consultations and ICU team members, and operational and environmental issues. We provide recommendations for future research and work-tools to support translation of the recommendations into practice. Conclusions: These guidelines identify the evidence base for best practices for family-centered care in the ICU. All recommendations were weak, highlighting the relative nascency of this field of research and the importance of future research to identify the most effective interventions to improve this important aspect of ICU care.

Predictors of hospital mortality in a population-based cohort of patients with acute lung injury

Objective:Studies describing predictors of mortality in patients with acute lung injury were primarily derived from selected academic centers. We sought to determine the predictors of mortality in a population-based cohort of patients with acute lung injury and to characterize the performance of current severity of illness scores in this population. Design:Secondary analysis of a prospective, multicenter, population-based cohort. Setting:Twenty-one hospitals in Washington State. Patients:The cohort included 1,113 patients with acute lung injury identified during the year 1999–2000. Interventions:None. Measurements and Main Results:We evaluated physiology, comorbidities, risk factors for acute lung injury, and other variables for their association with death at hospital discharge. Bivariate predictors of death were entered into a multiple logistic regression model. We compared Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE III, and Simplified Acute Physiology Score II to the multivariable model using area under the receiver operating characteristic curve. The model was validated in an independent cohort of 886 patients with acute lung injury. Modified acute physiology score, age, comorbidities, arterial pH, minute ventilation, Paco2, Pao2/Fio2 ratio, intensive care unit admission source, and intensive care unit days before onset of acute lung injury were independently predictive of in-hospital death (p < .05). The area under the receiver operating characteristic curve for the multivariable model was superior to that of APACHE III (.81 vs. .77, p < .001) but was no different after external validation (.71 vs. .70, p = .64). Conclusions:The predictors of mortality in patients with acute lung injury are similar to those predictive of mortality in the general intensive care unit population, indicating disease heterogeneity within this cohort. Accordingly, APACHE III predicts mortality in acute lung injury as well as a model using variables selected specifically for patients with acute lung injury.

The validity of using ICD-9 codes and pharmacy records to identify patients with chronic obstructive pulmonary disease

BackgroundAdministrative data is often used to identify patients with chronic obstructive pulmonary disease (COPD), yet the validity of this approach is unclear. We sought to develop a predictive model utilizing administrative data to accurately identify patients with COPD.MethodsSequential logistic regression models were constructed using 9573 patients with postbronchodilator spirometry at two Veterans Affairs medical centers (2003-2007). COPD was defined as: 1) FEV1/FVC <0.70, and 2) FEV1/FVC < lower limits of normal. Model inputs included age, outpatient or inpatient COPD-related ICD-9 codes, and the number of metered does inhalers (MDI) prescribed over the one year prior to and one year post spirometry. Model performance was assessed using standard criteria.Results4564 of 9573 patients (47.7%) had an FEV1/FVC < 0.70. The presence of ≥1 outpatient COPD visit had a sensitivity of 76% and specificity of 67%; the AUC was 0.75 (95% CI 0.74-0.76). Adding the use of albuterol MDI increased the AUC of this model to 0.76 (95% CI 0.75-0.77) while the addition of ipratropium bromide MDI increased the AUC to 0.77 (95% CI 0.76-0.78). The best performing model included: ≥6 albuterol MDI, ≥3 ipratropium MDI, ≥1 outpatient ICD-9 code, ≥1 inpatient ICD-9 code, and age, achieving an AUC of 0.79 (95% CI 0.78-0.80).ConclusionCommonly used definitions of COPD in observational studies misclassify the majority of patients as having COPD. Using multiple diagnostic codes in combination with pharmacy data improves the ability to accurately identify patients with COPD.

Paramedic Training for Proficient Prehospital Endotracheal Intubation

Abstract Background. Emergency airway management is an important component of resuscitation of critically ill patients. Multiple studies demonstrate variable endotracheal intubation (ETI) success by prehospital providers. Data describing how many ETI training experiences are required to achieve high success rates are sparse. Objectives. To describe the relationship between the number of prehospital ETI experiences and the likelihood of success on subsequent ETI and to specifically look at uncomplicated first-pass ETI in a university-based training program with substantial resources. Methods. We conducted a secondary analysis of a prospectively collected cohort of paramedic student prehospital intubation attempts. Data collected on prehospital ETIs included indication, induction agents, number of direct laryngoscopy attempts, and advanced airway procedures performed. We used multivariable generalized estimating equations (GEE) analysis to determine the effect of cumulative ETI experience on first-pass and overall ETI success rates. Results. Over a period of three years, 56 paramedic students attempted 576 prehospital ETIs. The odds of overall ETI success were associated with cumulative ETI experience (odds ratio [OR] 1.097 per encounter, 95% confidence interval [CI] = 1.026–1.173, p = 0.006). The odds of first-pass ETI success were associated with cumulative ETI experience (OR 1.061 per encounter, 95% CI = 1.014–1.109, p = 0.009). Conclusion. In a training program with substantial clinical opportunities and resources, increased ETI success rates were associated with increasing clinical exposure. However, first-pass placement of the ETT with a high success rate requires high numbers of ETI training experiences that may exceed the number available in many training programs.

Using existing data to address important clinical questions in critical care

Objective:With important technological advances in healthcare delivery and the Internet, clinicians and scientists now have access to overwhelming number of available databases capturing patients with critical illness. Yet, investigators seeking to answer important clinical or research questions with existing data have few resources that adequately describe the available sources and the strengths and limitations of each. This article reviews an approach to selecting a database to address health services and outcomes research questions in critical care, examines several databases that are commonly used for this purpose, and briefly describes some strengths and limitations of each. Data Sources:Narrative review of the medical literature. Summary:The available databases that collect information on critically ill patients are numerous and vary in the types of questions they can optimally answer. Selection of a data source must consider not only accessibility but also the quality of the data contained within the database, and the extent to which it captures the necessary variables for the research question. Questions seeking causal associations (e.g., effect of treatment on mortality) usually either require secondary data that contain detailed information about demographics, laboratories, and physiology to best address nonrandom selection or sophisticated study design. Purely descriptive questions (e.g., incidence of respiratory failure) can often be addressed using secondary data with less detail such as administrative claims. Although each database has its own inherent limitations, all secondary analyses will be subject to the same challenges of appropriate study design and good observational research. Conclusion:The literature demonstrates that secondary analyses can have significant impact on critical care practice. While selection of the optimal database for a particular question is a necessary part of high-quality analyses, it is not sufficient to guarantee an unbiased study. Thoughtful and well-constructed study design and analysis approaches remain equally important pillars of robust science. Only through responsible use of existing data will investigators ensure that their study has the greatest impact on critical care practice and outcomes.

Hospital‐Level Variation in the Use of Intensive Care

OBJECTIVE To determine the extent to which hospitals vary in the use of intensive care, and the proportion of variation attributable to differences in hospital practice that is independent of known patient and hospital factors. DATA SOURCE Hospital discharge data in the State Inpatient Database for Maryland and Washington States in 2006. STUDY DESIGN Cross-sectional analysis of 90 short-term, acute care hospitals with critical care capabilities. DATA COLLECTION/METHODS: We quantified the proportion of variation in intensive care use attributable to hospitals using intraclass correlation coefficients derived from mixed-effects logistic regression models after successive adjustment for known patient and hospital factors. PRINCIPAL FINDINGS The proportion of hospitalized patients admitted to an intensive care unit (ICU) across hospitals ranged from 3 to 55 percent (median 12 percent; IQR: 9, 17 percent). After adjustment for patient factors, 19.7 percent (95 percent CI: 15.1, 24.4) of total variation in ICU use across hospitals was attributable to hospitals. When observed hospital characteristics were added, the proportion of total variation in intensive care use attributable to unmeasured hospital factors decreased by 26-14.6 percent (95 percent CI: 11, 18.3 percent). CONCLUSIONS Wide variability exists in the use of intensive care across hospitals, not attributable to known patient or hospital factors, and may be a target to improve efficiency and quality of critical care.

Variation in use of intensive care for adults with diabetic ketoacidosis

Objective:Intensive care unit beds are limited, yet few guidelines exist for triage of patients to the intensive care unit, especially patients at low risk for mortality. The frequency with which low-risk patients are admitted to intensive care units in different hospitals is unknown. Our objective was to assess variation in the use of intensive care for patients with diabetic ketoacidosis, a common condition with a low risk of mortality. Design:Observational study using the New York State In-patient Database (2005–2007). Setting:One hundred fifty-nine New York State acute care hospitals. Patients:Fifteen thousand nine hundred ninety-four adult (≥18) hospital admissions with a primary diagnosis of diabetic ketoacidosis (International Classification of Diseases, Ninth Revision, Clinical Modification 250.1x). Interventions:None. Measurements and Main Results:We calculated reliability- and risk-adjusted intensive care unit utilization, hospital length of stay, and mortality. We identified hospital-level factors associated with increased likelihood of intensive care unit admission after controlling patient characteristics using multilevel, mixed-effects logistic regression analyses; we assessed the amount of residual variation in intensive care unit utilization using the intraclass correlation coefficient. Use of intensive care for diabetic ketoacidosis patients varied widely across hospitals (adjusted range: 2.1% to 87.7%), but was not associated with hospital length of stay or mortality. After multilevel adjustment, hospitals with a high volume of diabetic ketoacidosis admissions admitted diabetic ketoacidosis patients to the intensive care unit less often (odds ratio 0.40, p = .002, highest quintile compared to lowest), whereas hospitals with higher rates of intensive care unit utilization for all nondiabetic ketoacidosis in-patients admitted diabetic ketoacidosis patients to the intensive care unit more frequently (odds ratio 1.31, p = .001, for each additional 10% increase). In the multilevel model, more than half (58%) of the variation in the intensive care unit admission practice attributable to hospitals remained unexplained. Conclusions:We observed variations across hospitals in the use of intensive care for diabetic ketoacidosis patients that was not associated with differences in-hospital length of stay or mortality. Institutional practice patterns appear to impact admission decisions and represent a potential target for reduction of resource utilization in higher use institutions.