Ten Years of Experience Training Non-Physician Anesthesia Providers in Haiti.

Surgery is increasingly recognized as an effective means of treating a proportion of the global burden of disease, especially in resource-limited countries. Often non-physicians, such as nurses, provide the majority of anesthesia; however, their training and formal supervision is often of low priority or even non-existent. To increase the number of safe anesthesia providers in Haiti, Médecins Sans Frontières has trained nurse anesthetists (NAs) for over 10 years. This article describes the challenges, outcomes, and future directions of this training program. From 1998 to 2008, 24 students graduated. Nineteen (79%) continue to work as NAs in Haiti and 5 (21%) have emigrated. In 2008, NAs were critical in providing anesthesia during a post-hurricane emergency where they performed 330 procedures. Mortality was 0.3% and not associated with lack of anesthesiologist supervision. The completion rate of this training program was high and the majority of graduates continue to work as nurse anesthetists in Haiti. Successful training requires a setting with a sufficient volume and diversity of operations, appropriate anesthesia equipment, a structured and comprehensive training program, and recognition of the training program by the national ministry of health and relevant professional bodies. Preliminary outcomes support findings elsewhere that NAs can be a safe and effective alternative where anesthesiologists are scarce. Training non-physician anesthetists is a feasible and important way to scale up surgical services resource limited settings

APACHE III outcome prediction in patients admitted to the intensive care unit after liver transplantation: a retrospective cohort study

<p>Abstract</p> <p>Background</p> <p>The Acute Physiology and Chronic Health Evaluation (APACHE) III prognostic system has not been previously validated in patients admitted to the intensive care unit (ICU) after orthotopic liver transplantation (OLT). We hypothesized that APACHE III would perform satisfactorily in patients after OLT</p> <p>Methods</p> <p>A retrospective cohort study was performed. Patients admitted to the ICU after OLT between July 1996 and May 2008 were identified. Data were abstracted from the institutional APACHE III and liver transplantation databases and individual patient medical records. Standardized mortality ratios (with 95% confidence intervals) were calculated by dividing the observed mortality rates by the rates predicted by APACHE III. The area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow C statistic were used to assess, respectively, discrimination and calibration of APACHE III.</p> <p>Results</p> <p>APACHE III data were available for 918 admissions after OLT. Mean (standard deviation [SD]) APACHE III (APIII) and Acute Physiology (APS) scores on the day of transplant were 60.5 (25.8) and 50.8 (23.6), respectively. Mean (SD) predicted ICU and hospital mortality rates were 7.3% (15.4) and 10.6% (18.9), respectively. The observed ICU and hospital mortality rates were 1.1% and 3.4%, respectively. The standardized ICU and hospital mortality ratios with their 95% C.I. were 0.15 (0.07 to 0.27) and 0.32 (0.22 to 0.45), respectively.</p> <p>There were statistically significant differences in APS, APIII, predicted ICU and predicted hospital mortality between survivors and non-survivors. In predicting mortality, the AUC of APACHE III prediction of hospital death was 0.65 (95% CI, 0.62 to 0.68). The Hosmer-Lemeshow C statistic was 5.288 with a p value of 0.871 (10 degrees of freedom).</p> <p>Conclusion</p> <p>APACHE III discriminates poorly between survivors and non-survivors of patients admitted to the ICU after OLT. Though APACHE III has been shown to be valid in heterogenous populations and in certain groups of patients with specific diagnoses, it should be used with caution – if used at all – in recipients of liver transplantation.</p

A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay

<p>Abstract</p> <p>Background</p> <p>Patients with a prolonged intensive care unit (ICU) length of stay account for a disproportionate amount of resource use. Early identification of patients at risk for a prolonged length of stay can lead to quality enhancements that reduce ICU stay. This study developed and validated a model that identifies patients at risk for a prolonged ICU stay.</p> <p>Methods</p> <p>We performed a retrospective cohort study of 343,555 admissions to 83 ICUs in 31 U.S. hospitals from 2002-2007. We examined the distribution of ICU length of stay to identify a threshold where clinicians might be concerned about a prolonged stay; this resulted in choosing a 5-day cut-point. From patients remaining in the ICU on day 5 we developed a multivariable regression model that predicted remaining ICU stay. Predictor variables included information gathered at admission, day 1, and ICU day 5. Data from 12,640 admissions during 2002-2005 were used to develop the model, and the remaining 12,904 admissions to internally validate the model. Finally, we used data on 11,903 admissions during 2006-2007 to externally validate the model.</p> <p>Results</p> <p>The variables that had the greatest impact on remaining ICU length of stay were those measured on day 5, not at admission or during day 1. Mechanical ventilation, PaO₂: FiO₂ratio, other physiologic components, and sedation on day 5 accounted for 81.6% of the variation in predicted remaining ICU stay. In the external validation set observed ICU stay was 11.99 days and predicted total ICU stay (5 days + day 5 predicted remaining stay) was 11.62 days, a difference of 8.7 hours. For the same patients, the difference between mean observed and mean predicted ICU stay using the APACHE day 1 model was 149.3 hours. The new model's r²was 20.2% across individuals and 44.3% across units.</p> <p>Conclusions</p> <p>A model that uses patient data from ICU days 1 and 5 accurately predicts a prolonged ICU stay. These predictions are more accurate than those based on ICU day 1 data alone. The model can be used to benchmark ICU performance and to alert physicians to explore care alternatives aimed at reducing ICU stay.</p

Underweight is independently associated with mortality in post-operative and non-operative patients admitted to the intensive care unit: a retrospective study

BACKGROUND: Low and high body mass index (BMI) have been recently shown to be associated with increased and decreased mortality after ICU admission, respectively. The objective of this study was to determine the impact of BMI on mortality and length of stay in patients admitted to the intensive care unit (ICU). METHODS: In this retrospective cohort study, the Acute Physiology and Chronic Health Evaluation (APACHE) III database of patients admitted to the ICUs of a tertiary academic medical center, from January 1997 to September 2002, was crossed with a Hospital Rule-based Systems database to obtain the height and weight of the patients on admission to the ICU. The cohort was divided in post-operative and non-operative groups. We created the following five subgroups based on the BMI: <18.5, 18.5 to 24.9, 25 to 29.9, 30.0 to 39.9, ≥ 40.0 Kg/m(2). A multiple logistic regression analysis was used to determine the independent impact of BMI on hospital mortality. The ICU length of stay ratio was defined as the ratio of the observed to the predicted LOS. P-value < 0.05 was considered significant. The 95% confidence interval (CI) was calculated for the odds ratio (OR). RESULTS: BMI was available in 19,669 of the 21,790 patients in the APACHE III database; 11,215 (57%) of the patients were admitted post-operatively. BMI < 18.5 was associated with increased mortality in both post-operative (OR = 2.14, 95% CI, 1.39 to 3.28) and non-operative (OR = 1.51, 95% CI, 1.13 to 2.01) patients. Post-operative patients with a BMI between 30.0 to 39.9 had a lower mortality rate (OR = 0.68, 95% CI, 0.49 to 0.94). Post-operative patients with BMI <18.5 or BMI ≥ 40 had an ICU length of stay ratio significantly higher than patients with BMI between 18.5 to 24.9. The addition of BMI < 18.5 did not improve significantly the accuracy of our prognostic model in predicting hospital mortality. CONCLUSIONS: Low BMI is associated with higher mortality in both post- and non-operative patients admitted to the ICU. LOS is increased in post-operative patients with low and high BMIs

Estimating Long-Term Survival of Critically Ill Patients: The PREDICT Model

BACKGROUND: Long-term survival outcome of critically ill patients is important in assessing effectiveness of new treatments and making treatment decisions. We developed a prognostic model for estimation of long-term survival of critically ill patients. METHODOLOGY AND PRINCIPAL FINDINGS: This was a retrospective linked data cohort study involving 11,930 critically ill patients who survived more than 5 days in a university teaching hospital in Western Australia. Older age, male gender, co-morbidities, severe acute illness as measured by Acute Physiology and Chronic Health Evaluation II predicted mortality, and more days of vasopressor or inotropic support, mechanical ventilation, and hemofiltration within the first 5 days of intensive care unit admission were associated with a worse long-term survival up to 15 years after the onset of critical illness. Among these seven pre-selected predictors, age (explained 50% of the variability of the model, hazard ratio [HR] between 80 and 60 years old = 1.95) and co-morbidity (explained 27% of the variability, HR between Charlson co-morbidity index 5 and 0 = 2.15) were the most important determinants. A nomogram based on the pre-selected predictors is provided to allow estimation of the median survival time and also the 1-year, 3-year, 5-year, 10-year, and 15-year survival probabilities for a patient. The discrimination (adjusted c-index = 0.757, 95% confidence interval 0.745-0.769) and calibration of this prognostic model were acceptable. SIGNIFICANCE: Age, gender, co-morbidities, severity of acute illness, and the intensity and duration of intensive care therapy can be used to estimate long-term survival of critically ill patients. Age and co-morbidity are the most important determinants of long-term prognosis of critically ill patients

Stratification of the severity of critically ill patients with classification trees

<p>Abstract</p> <p>Background</p> <p>Development of three classification trees (CT) based on the CART (<it>Classification and Regression Trees</it>), CHAID (<it>Chi-Square Automatic Interaction Detection</it>) and C4.5 methodologies for the calculation of probability of hospital mortality; the comparison of the results with the APACHE II, SAPS II and MPM II-24 scores, and with a model based on multiple logistic regression (LR).</p> <p>Methods</p> <p>Retrospective study of 2864 patients. Random partition (70:30) into a Development Set (DS) n = 1808 and Validation Set (VS) n = 808. Their properties of discrimination are compared with the ROC curve (AUC CI 95%), Percent of correct classification (PCC CI 95%); and the calibration with the Calibration Curve and the Standardized Mortality Ratio (SMR CI 95%).</p> <p>Results</p> <p>CTs are produced with a different selection of variables and decision rules: CART (5 variables and 8 decision rules), CHAID (7 variables and 15 rules) and C4.5 (6 variables and 10 rules). The common variables were: inotropic therapy, Glasgow, age, (A-a)O2 gradient and antecedent of chronic illness. In VS: all the models achieved acceptable discrimination with AUC above 0.7. CT: CART (0.75(0.71-0.81)), CHAID (0.76(0.72-0.79)) and C4.5 (0.76(0.73-0.80)). PCC: CART (72(69-75)), CHAID (72(69-75)) and C4.5 (76(73-79)). Calibration (SMR) better in the CT: CART (1.04(0.95-1.31)), CHAID (1.06(0.97-1.15) and C4.5 (1.08(0.98-1.16)).</p> <p>Conclusion</p> <p>With different methodologies of CTs, trees are generated with different selection of variables and decision rules. The CTs are easy to interpret, and they stratify the risk of hospital mortality. The CTs should be taken into account for the classification of the prognosis of critically ill patients.</p

A comparison between the APACHE II and Charlson Index Score for predicting hospital mortality in critically ill patients

<p>Abstract</p> <p>Background</p> <p>Risk adjustment and mortality prediction in studies of critical care are usually performed using acuity of illness scores, such as Acute Physiology and Chronic Health Evaluation II (APACHE II), which emphasize physiological derangement. Common risk adjustment systems used in administrative datasets, like the Charlson index, are entirely based on the presence of co-morbid illnesses. The purpose of this study was to compare the discriminative ability of the Charlson index to the APACHE II in predicting hospital mortality in adult multisystem ICU patients.</p> <p>Methods</p> <p>This was a population-based cohort design. The study sample consisted of adult (>17 years of age) residents of the Calgary Health Region admitted to a multisystem ICU between April 2002 and March 2004. Clinical data were collected prospectively and linked to hospital outcome data. Multiple regression analyses were used to compare the performance of APACHE II and the Charlson index.</p> <p>Results</p> <p>The Charlson index was a poor predictor of mortality (C = 0.626). There was minimal difference between a baseline model containing age, sex and acute physiology score (C = 0.74) and models containing either chronic health points (C = 0.76) or Charlson index variations (C = 0.75, 0.76, 0.77). No important improvement in prediction occurred when the Charlson index was added to the full APACHE II model (C = 0.808 to C = 0.813).</p> <p>Conclusion</p> <p>The Charlson index does not perform as well as the APACHE II in predicting hospital mortality in ICU patients. However, when acuity of illness scores are unavailable or are not recorded in a standard way, the Charlson index might be considered as an alternative method of risk adjustment and therefore facilitate comparisons between intensive care units.</p

Genome-wide analysis of ivermectin response by Onchocerca volvulus reveals that genetic drift and soft selective sweeps contribute to loss of drug sensitivity

Treatment of onchocerciasis using mass ivermectin administration has reduced morbidity and transmission throughout Africa and Central/South America. Mass drug administration is likely to exert selection pressure on parasites, and phenotypic and genetic changes in several Onchocerca volvulus populations from Cameroon and Ghana-exposed to more than a decade of regular ivermectin treatment-have raised concern that sub-optimal responses to ivermectin's anti-fecundity effect are becoming more frequent and may spread.Pooled next generation sequencing (Pool-seq) was used to characterise genetic diversity within and between 108 adult female worms differing in ivermectin treatment history and response. Genome-wide analyses revealed genetic variation that significantly differentiated good responder (GR) and sub-optimal responder (SOR) parasites. These variants were not randomly distributed but clustered in ~31 quantitative trait loci (QTLs), with little overlap in putative QTL position and gene content between the two countries. Published candidate ivermectin SOR genes were largely absent in these regions; QTLs differentiating GR and SOR worms were enriched for genes in molecular pathways associated with neurotransmission, development, and stress responses. Finally, single worm genotyping demonstrated that geographic isolation and genetic change over time (in the presence of drug exposure) had a significantly greater role in shaping genetic diversity than the evolution of SOR.This study is one of the first genome-wide association analyses in a parasitic nematode, and provides insight into the genomics of ivermectin response and population structure of O. volvulus. We argue that ivermectin response is a polygenically-determined quantitative trait (QT) whereby identical or related molecular pathways but not necessarily individual genes are likely to determine the extent of ivermectin response in different parasite populations. Furthermore, we propose that genetic drift rather than genetic selection of SOR is the underlying driver of population differentiation, which has significant implications for the emergence and potential spread of SOR within and between these parasite populations

A Comparison of Administrative and Physiologic Predictive Models in Determining Risk Adjusted Mortality Rates in Critically Ill Patients

Hospitals are increasingly compared based on clinical outcomes adjusted for severity of illness. Multiple methods exist to adjust for differences between patients. The challenge for consumers of this information, both the public and healthcare providers, is interpreting differences in risk adjustment models particularly when models differ in their use of administrative and physiologic data. We set to examine how administrative and physiologic models compare to each when applied to critically ill patients.We prospectively abstracted variables for a physiologic and administrative model of mortality from two intensive care units in the United States. Predicted mortality was compared through the Pearsons Product coefficient and Bland-Altman analysis. A subgroup of patients admitted directly from the emergency department was analyzed to remove potential confounding changes in condition prior to ICU admission.We included 556 patients from two academic medical centers in this analysis. The administrative model and physiologic models predicted mortalities for the combined cohort were 15.3% (95% CI 13.7%, 16.8%) and 24.6% (95% CI 22.7%, 26.5%) (t-test p-value<0.001). The r(2) for these models was 0.297. The Bland-Atlman plot suggests that at low predicted mortality there was good agreement; however, as mortality increased the models diverged. Similar results were found when analyzing a subgroup of patients admitted directly from the emergency department. When comparing the two hospitals, there was a statistical difference when using the administrative model but not the physiologic model. Unexplained mortality, defined as those patients who died who had a predicted mortality less than 10%, was a rare event by either model.In conclusion, while it has been shown that administrative models provide estimates of mortality that are similar to physiologic models in non-critically ill patients with pneumonia, our results suggest this finding can not be applied globally to patients admitted to intensive care units. As patients and providers increasingly use publicly reported information in making health care decisions and referrals, it is critical that the provided information be understood. Our results suggest that severity of illness may influence the mortality index in administrative models. We suggest that when interpreting "report cards" or metrics, health care providers determine how the risk adjustment was made and compares to other risk adjustment models

Maintenance of the pectoralis muscle during hibernation in the big brown bat, Eptesicus fuscus

The relationship between pectoralis muscle mass and body mass is examined throughout the annual body mass cycle in Eptesicus fuscus in order to evaluate muscle maintenance during hibernation. E. fuscus undergoes large fluctuations in body mass during the year due to pregnancy, parturition, prehibernation fattening, and hibernation (Table 1). Parallel changes occur in pectoralis muscle mass and total pectoralis protein mass (Table 2). The strong correlation between log pectoralis mass and log body mass (Fig. 3) and the lack of correlation between pectoralis mass and forearm length (Fig. 1, 2) suggest that the seasonal variation in pectoralis muscle mass represents a compensatory response to changing body mass. In active bats this relationship closely resembles the compensatory response predicted by flight theory.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47128/1/360_2004_Article_BF00689733.pd