Stanley Lemeshow

A New Simplified Acute Physiology Score (SAPS II) Based on a European/North American Multicenter Study

OBJECTIVE To develop and validate a new Simplified Acute Physiology Score, the SAPS II, from a large sample of surgical and medical patients, and to provide a method to convert the score to a probability of hospital mortality. DESIGN AND SETTING The SAPS II and the probability of hospital mortality were developed and validated using data from consecutive admissions to 137 adult medical and/or surgical intensive care units in 12 countries. PATIENTS The 13,152 patients were randomly divided into developmental (65%) and validation (35%) samples. Patients younger than 18 years, burn patients, coronary care patients, and cardiac surgery patients were excluded. OUTCOME MEASURE Vital status at hospital discharge. RESULTS The SAPS II includes only 17 variables: 12 physiology variables, age, type of admission (scheduled surgical, unscheduled surgical, or medical), and three underlying disease variables (acquired immunodeficiency syndrome, metastatic cancer, and hematologic malignancy). Goodness-of-fit tests indicated that the model performed well in the developmental sample and validated well in an independent sample of patients (P = .883 and P = .104 in the developmental and validation samples, respectively). The area under the receiver operating characteristic curve was 0.88 in the developmental sample and 0.86 in the validation sample. CONCLUSION The SAPS II, based on a large international sample of patients, provides an estimate of the risk of death without having to specify a primary diagnosis. This is a starting point for future evaluation of the efficiency of intensive care units.

A COMPARISON OF GOODNESS-OF-FIT TESTS FOR THE LOGISTIC REGRESSION MODEL

Recent work has shown that there may be disadvantages in the use of the chi-square-like goodness-of-fit tests for the logistic regression model proposed by Hosmer and Lemeshow that use fixed groups of the estimated probabilities. A particular concern with these grouping strategies based on estimated probabilities, fitted values, is that groups may contain subjects with widely different values of the covariates. It is possible to demonstrate situations where one set of fixed groups shows the model fits while the test rejects fit using a different set of fixed groups. We compare the performance by simulation of these tests to tests based on smoothed residuals proposed by le Cessie and Van Houwelingen and Royston, a score test for an extended logistic regression model proposed by Stukel, the Pearson chi-square and the unweighted residual sum-of-squares. These simulations demonstrate that all but one of Roystons tests have the correct size. An examination of the performance of the tests when the correct model has a quadratic term but a model containing only the linear term has been fit shows that the Pearson chi-square, the unweighted sum-of-squares, the Hosmer-Lemeshow decile of risk, the smoothed residual sum-of-squares and Stukels score test, have power exceeding 50 per cent to detect moderate departures from linearity when the sample size is 100 and have power over 90 per cent for these same alternatives for samples of size 500. All tests had no power when the correct model had an interaction between a dichotomous and continuous covariate but only the continuous covariate model was fit. Power to detect an incorrectly specified link was poor for samples of size 100. For samples of size 500 Stukels score test had the best power but it only exceeded 50 per cent to detect an asymmetric link function. The power of the unweighted sum-of-squares test to detect an incorrectly specified link function was slightly less than Stukels score test. We illustrate the tests within the context of a model for factors associated with low birth weight.

Sample size determination in health studies : A practical manual

The sample size calculation for a prevalence only needs a simple formula. However, there are a number of practical issues in selecting values for the parameters required in the formula. Several Practical Manual. Geneva: International Journal of Health Promotion and Education 11/2014, 53(3):128-135. Studies investigating the influence of genetic variants in vitamin D binding protein (DBP) and Sample size determination in health studies: A practical manual. Sample size determination in health studies. a practical manual. Sample size determination in health studies. a practical manual. PDF Came out some time. Targeted public health messages to raise knowledge level, correct misconception and Sample size determination in health studies : a practical manual. Sample size determination in health studies: a practical manual. 1991 Vol 0. S Lemeshow, S K Lwanga. The minimum sample size of 385 respondents was. The sample size needed for each of the selected province if we want to have a Sample size determination in health studies: A practical manual. Geneva:. Sample Size Determination In Health Studies A Practical Manual Read/Download Ethical approval has been obtained from the State Secretary of Health. Lemeshow S Sample size determination in health studies: a practical manual. Geneva. Handbook for Mental health posting. Obafemi (13), Lwanga, S.K. and Lemeshow, S. (1991) Sample Size Determination in Health Studies: A Practical Manual. The sample size was determine using the formula for prevalence study described in S. Sample size determination in health studies: a practical manual. Sample size determination and sampling procedure Lwanga SK, Lemeshow S. Sample Size Determination for Health Studies: A Practical Manual. Geneva. S. K. Wanga and S. Lemeshow, Sample Size Determination in Health Studies. A Practical Manual, World Health Organization, Geneva, Switzerland, 1991. online, Research questions, hypotheses and objectives (Practical Tips for Sample Size Determination in Health Studies: A Practical Manual WHO pdf, The. More recently, randomized controlled studies comparing SMS, phone calls, and no S. Sample size determination in health studies: a practical manual. Sample size determination in health studies: a practical manual. World Health. Organization. Riegert-Johnson DL, Korf BR, Alford RL, Broder MI, Keats BJ. Ministry of Public Health and Sanitation: National Strategy on Infant and Young Child Feeding Strategy 2007-2010. S. K. Lwanga, and S. Lameshow, “Sample size determination in health studies. A practical manual”. pp 1-71, 1991. Survey methods in community medicine, epidemiological studies, Lwanga SK, Lemeshow S. Sample size determination in health studies, a practical manual. As per WHO guidelines, 3 a minimum sample size of 96 was required using anticipated S. Sample size determination in health studies: A practical manual. The objective of this survey study was to determine the prevalence Lemeshow S. Sample size determination in health studies: a practical manual. 1991. 11. The sample size was computed using the formula for estimating a population S. Sample size determination in health studies: A practical manual. Geneva:. Aims: To know the factors determining gender preference by pregnant women, Lemshaw S. Sample size determination in health studies:A practical Manual. To determine the prevalence of mental health problems and psychosocial Sample size calculation for In other studies, the prevaA Practical Manual. In testing of hypothesis studies, the objective of sample size calculation is to achieve a S. Sample Size Determination in Health Studies: A Practical Manual. women are left with chronic ill health and 1 million neonatal deaths occur. and Lemeshow S, Sample size determination in health studies: A practical manual. In this state, a significant obstacle to health care access is the huge distances between the S. Sample size determination in health studies: a practical manual. International Journal of Interdisciplinary and Multidisciplinary Studies (IJIMS), S.Sample Size Determination in Health Studies–A Practical Manual: World. Bull World Health Organ 2002,80:546-54. (Pubmed) Lwanga SK, Lemeshow S. Sample Size Determination in Health Studies: A Practical Manual. Geneva:. Ian Janssen, Professor, School of Kinesiology and Health Studies, Queens University S. Sample size determination in health studies: a practical manual. The sample was calculated using the using the World Health Organization Sample Size Determination in Health Studies (17) assuming a 41% prevalence. Lwanga S K, Lemeshow S. Sample size determination in health studies. A practical manual. World Health Organization Document 1991,1-80. pdf, Rieder H L. Sample size was estimated using the World Health Organization formula for sample size S. Sample size determination in health studies: a practical manual.

Confidence Interval Estimation of Interaction

Relative excess risk due to interaction, the proportion of disease among those with both exposures that is attributable to their interaction, and the synergy index have been proposed as measures of interaction in epidemiologic studies. This paper presents the methodology for obtaining confidence interval estimates of these indices utilizing routinely available output from multiple logistic regression software.

Adequacy of sample size in health studies

Part 1 Statistical methods for sample size determination: the one sample problem the two sample problem sample size for case-control studies sample size determination for cohort studies lot quality assurance sampling the incidence density sample size for continuous response variables sample size for sample surveys. Part 2 Foundations of sampling and statistical theory: the population the sample sampling distribution characteristics of estimates of population parameters hypothesis testing two sample confidence intervals and hypothesis tests epidemiologic study design basis sampling concepts.

Acquired Weakness, Handgrip Strength, and Mortality in Critically Ill Patients

RATIONALE ICU-acquired paresis (ICUAP) is common in survivors of critical illness. There is significant associated morbidity, including prolonged time on the ventilator and longer hospital stay. However, it is unclear whether ICUAP is independently associated with mortality, as sicker patients are more prone and existing studies have not adjusted for this. OBJECTIVES To test the hypothesis that ICUAP is independently associated with increased mortality. Secondarily, to determine if handgrip dynamometry is a concise measure of global strength and is independently associated with mortality. METHODS A prospective multicenter cohort study was conducted in intensive care units (ICU) of five academic medical centers. Adults requiring at least 5 days of mechanical ventilation without evidence of preexisting neuromuscular disease were followed until awakening and were then examined for strength. MEASUREMENTS AND MAIN RESULTS We measured global strength and handgrip dynamometry. The primary outcome was in-hospital mortality and secondary outcomes were hospital and ICU-free days, ICU readmission, and recurrent respiratory failure. Subjects with ICUAP (average MRC score of < 4) had longer hospital stays and required mechanical ventilation longer. Handgrip strength was lower in subjects with ICUAP and had good test performance for diagnosing ICUAP. After adjustment for severity of illness, ICUAP was independently associated with hospital mortality (odds ratio [OR], 7.8; 95% confidence interval [CI], 2.4-25.3; P = 0.001). Separately, handgrip strength was independently associated with hospital mortality (OR, 4.5; 95% CI, 1.5-13.6; P = 0.007). CONCLUSIONS ICUAP is independently associated with increased hospital mortality. Handgrip strength is also independently associated with poor hospital outcome and may serve as a simple test to identify ICUAP. Clinical trial registered with www.clinicaltrials.gov (NCT00106665).

Glucose variability and mortality in patients with sepsis

Objective:Treatment and prevention of hyperglycemia has been advocated for subjects with sepsis. Glucose variability, rather than the glucose level, has also been shown to be an important factor associated with in-hospital mortality, in general, critically ill patients. Our objective was to determine the association between glucose variability and hospital mortality in septic patients and the expression of glucose variability that best reflects this risk. Design:Retrospective, single-center cohort study. Setting:Academic, tertiary care hospital. Patients:Adult subjects hospitalized for >1 day, with a diagnosis of sepsis were included. Interventions:None. Measurements:Glucose variability was calculated for all subjects as the average and standard deviation of glucose, the mean amplitude of glycemic excursions, and the glycemic lability index. Hospital mortality was the primary outcome variable. Logistic regression was used to determine the odds of hospital death in relation to measures of glucose variability after adjustment for important covariates. Main results:Of the methods used to measure glucose variability, the glycemic lability index had the best discrimination for mortality (area under the curve = 0.67, p < 0.001). After adjustment for confounders, including the number of organ failures and the occurrence of hypoglycemia, there was a significant interaction between glycemic lability index and average glucose level, and the odds of hospital mortality. Higher glycemic lability index was not independently associated with mortality among subjects with average glucose levels above the median for the cohort. However, subjects with increased glycemic lability index, but lower average glucose values had almost five-fold increased odds of hospital mortality (odds ratio = 4.73, 95% confidence interval = 2.6-8.7) compared with those with lower glycemic lability index. Conclusions:Glucose variability is independently associated with hospital mortality in septic patients. Strategies to reduce glucose variability should be studied to determine whether they improve the outcomes of septic patients.

Efficacy and safety of the monoclonal anti-tumor necrosis factor antibody F(ab')2 fragment afelimomab in patients with severe sepsis and elevated interleukin-6 levels.

Objective:To evaluate whether administration of afelimomab, an anti-tumor necrosis factor F(ab′)2 monoclonal antibody fragment, would reduce 28-day all-cause mortality in patients with severe sepsis and elevated serum levels of IL-6. Design:Prospective, randomized, double-blind, placebo-controlled, multiple-center, phase III clinical trial. Setting:One hundred fifty-seven intensive care units in the United States and Canada. Patients:Subjects were 2,634 patients with severe sepsis secondary to documented infection, of whom 998 had elevated interleukin-6 levels. Interventions:Patients were stratified into two groups by means of a rapid qualitative interleukin-6 test kit designed to identify patients with serum interleukin-6 levels above (test positive) or below (test negative) approximately 1000 pg/mL. Of the 2,634 patients, 998 were stratified into the test-positive group, 1,636 into the test-negative group. They were then randomly assigned 1:1 to receive afelimomab 1 mg/kg or placebo for 3 days and were followed for 28 days. The a priori population for efficacy analysis was the group of patients with elevated baseline interleukin-6 levels as defined by a positive rapid interleukin-6 test result. Measurements and Main Results:In the group of patients with elevated interleukin-6 levels, the mortality rate was 243 of 510 (47.6%) in the placebo group and 213 of 488 (43.6%) in the afelimomab group. Using a logistic regression analysis, treatment with afelimomab was associated with an adjusted reduction in the risk of death of 5.8% (p = .041) and a corresponding reduction of relative risk of death of 11.9%. Mortality rates for the placebo and afelimomab groups in the interleukin-6 test negative population were 234 of 819 (28.6%) and 208 of 817 (25.5%), respectively. In the overall population of interleukin-6 test positive and negative patients, the placebo and afelimomab mortality rates were 477 of 1,329 (35.9%)and 421 of 1,305 (32.2%), respectively. Afelimomab resulted in a significant reduction in tumor necrosis factor and interleukin-6 levels and a more rapid improvement in organ failure scores compared with placebo. The safety profile of afelimomab was similar to that of placebo. Conclusions:Afelimomab is safe, biologically active, and well tolerated in patients with severe sepsis, reduces 28-day all-cause mortality, and attenuates the severity of organ dysfunction in patients with elevated interleukin-6 levels.

Norepinephrine Up-regulates the Expression of Vascular Endothelial Growth Factor, Matrix Metalloproteinase (MMP)-2, and MMP-9 in Nasopharyngeal Carcinoma Tumor Cells

Recent studies using ovarian cancer cells have shown that the catecholamine hormones norepinephrine (norepi) and epinephrine (epi) may influence cancer progression by modulating the expression of matrix metalloproteinases (MMP) and vascular endothelial growth factor (VEGF). The purpose of this study is to determine if the stress hormone norepi can influence the expression of MMP-2, MMP-9, and VEGF in nasopharyngeal carcinoma (NPC) tumors by using three NPC tumor cell lines. The NPC cell lines HONE-1, HNE-1, and CNE-1 were treated with norepi. The effects of norepi on MMP-2, MMP-9, and VEGF synthesis were measured by ELISA; functional MMP activity was measured by the invasive potential of the cells using a membrane invasion culture system whereas functional activity of VEGF was analyzed using a human umbilical vein endothelial cell tube formation assay. Norepi treatment increased MMP-2, MMP-9, and VEGF levels in culture supernatants of HONE-1 cells, which could be inhibited by the beta-blocker propranolol. Norepi induced the invasiveness of all NPC cell lines in a dose-dependent manner, which was blocked by CMT-3, an MMP inhibitor, and propranolol. Norepi stimulated the release of functional angiogenic VEGF by HONE-1 cells as well. Finally, HONE-1 cells were shown to express beta-adrenergic receptors as did seven of seven NPC biopsies examined. The data suggest that catecholamine hormones produced by the sympathetic-adrenal medullary axis may affect NPC tumor progression, in part, through modulation of key angiogenic cytokines.

Outcomes of the Surviving Sepsis Campaign in intensive care units in the USA and Europe: a prospective cohort study

BACKGROUND Mortality from severe sepsis and septic shock differs across continents, countries, and regions. We aimed to use data from the Surviving Sepsis Campaign (SSC) to compare models of care and outcomes for patients with severe sepsis and septic shock in the USA and Europe. METHODS The SSC was introduced into more than 200 sites in Europe and the USA. All patients identified with severe sepsis and septic shock in emergency departments or hospital wards and admitted to intensive care units (ICUs), and those with sepsis in ICUs were entered into the SSC database. Patients entered into the database from its launch in January, 2005, through January, 2010, in units with at least 20 patients and 3 months of enrolment of patients were included in this analysis. Patients included in the cohort were limited to those entered in the first 4 years at every site. We used random-effects logistic regression to estimate the hospital mortality odds ratio (OR) for Europe relative to the USA. We used random-effects linear regression to find the relation between lengths of stay in hospital and ICU and geographic region. FINDINGS 25 375 patients were included in the cohort. The USA included 107 sites with 18 766 (74%) patients, and Europe included 79 hospital sites with 6609 (26%) patients. In the USA, 12 218 (65·1%) were admitted to the ICU from the emergency department whereas in Europe, 3405 (51·5%) were admitted from the wards. The median stay on the hospital wards before ICU admission was longer in Europe than in the USA (1·0 vs 0·1 days, difference 0·9, 95% CI 0·8-0·9). Raw hospital mortality was higher in Europe than in the USA (41·1%vs 28·3%, difference 12·8, 95% CI 11·5-14·7). The median length of stay in ICU (7·8 vs 4·2 days, 3·6, 3·3-3·7) and hospital (22·8 vs 10·5 days, 12·3, 11·9-12·8) was longer in Europe than in the USA. Adjusted mortality in Europe was not significantly higher than that in the USA (32·3%vs 31·3%, 1·0, -1·7 to 3·7, p=0·468). Complete compliance with all applicable elements of the sepsis resuscitation bundle was higher in the USA than in Europe (21·6%vs 18·4%, 3·2, 2·2-4·4). INTERPRETATION The significant difference in unadjusted mortality and the fact that this difference disappears with severity adjustment raise important questions about the effect of the approach to critical care in Europe compared with that in the USA. The effect of ICU bed availability on outcomes in patients with severe sepsis and septic shock requires further investigation.