Bedside estimates of dead space using end-tidal CO2 are independently associated with mortality in ARDS.

PURPOSE: In acute respiratory distress syndrome (ARDS), dead space fraction has been independently associated with mortality. We hypothesized that early measurement of the difference between arterial and end-tidal CO2 (arterial-ET difference), a surrogate for dead space fraction, would predict mortality in mechanically ventilated patients with ARDS. METHODS: We performed two separate exploratory analyses. We first used publicly available databases from the ALTA, EDEN, and OMEGA ARDS Network trials (N = 124) as a derivation cohort to test our hypothesis. We then performed a separate retrospective analysis of patients with ARDS using University of Chicago patients (N = 302) as a validation cohort. RESULTS: The ARDS Network derivation cohort demonstrated arterial-ET difference, vasopressor requirement, age, and APACHE III to be associated with mortality by univariable analysis. By multivariable analysis, only the arterial-ET difference remained significant (P = 0.047). In a separate analysis, the modified Enghoff equation ((PaCO2-PETCO2)/PaCO2) was used in place of the arterial-ET difference and did not alter the results. The University of Chicago cohort found arterial-ET difference, age, ventilator mode, vasopressor requirement, and APACHE II to be associated with mortality in a univariate analysis. By multivariable analysis, the arterial-ET difference continued to be predictive of mortality (P = 0.031). In the validation cohort, substitution of the arterial-ET difference for the modified Enghoff equation showed similar results. CONCLUSION: Arterial to end-tidal CO2 (ETCO2) difference is an independent predictor of mortality in patients with ARDS.

Best Practices for Binary and Ordinal Data Analyses.

The measurement of many human traits, states, and disorders begins with a set of items on a questionnaire. The response format for these questions is often simply binary (e.g., yes/no) or ordered (e.g., high, medium or low). During data analysis, these items are frequently summed or used to estimate factor scores. In clinical applications, such assessments are often non-normally distributed in the general population because many respondents are unaffected, and therefore asymptomatic. As a result, in many cases these measures violate the statistical assumptions required for subsequent analyses. To reduce the influence of the non-normality and quasi-continuous assessment, variables are frequently recoded into binary (affected-unaffected) or ordinal (mild-moderate-severe) diagnoses. Ordinal data therefore present challenges at multiple levels of analysis. Categorizing continuous variables into ordered categories typically results in a loss of statistical power, which represents an incentive to the data analyst to assume that the data are normally distributed, even when they are not. Despite prior zeitgeists suggesting that, e.g., variables with more than 10 ordered categories may be regarded as continuous and analyzed as if they were, we show via simulation studies that this is not generally the case. In particular, using Pearson product-moment correlations instead of maximum likelihood estimates of polychoric correlations biases the estimated correlations towards zero. This bias is especially severe when a plurality of the observations fall into a single observed category, such as a score of zero. By contrast, estimating the ordinal correlation by maximum likelihood yields no estimation bias, although standard errors are (appropriately) larger. We also illustrate how odds ratios depend critically on the proportion or prevalence of affected individuals in the population, and therefore are sub-optimal for studies where comparisons of association metrics are needed. Finally, we extend these analyses to the classical twin model and demonstrate that treating binary data as continuous will underestimate genetic and common environmental variance components, and overestimate unique environment (residual) variance. These biases increase as prevalence declines. While modeling ordinal data appropriately may be more computationally intensive and time consuming, failing to do so will likely yield biased correlations and biased parameter estimates from modeling them.

Public Health Lessons Learned From Biases in Coronavirus Mortality Overestimation.

In testimony before US Congress on March 11, 2020, members of the House Oversight and Reform Committee were informed that estimated mortality for the novel coronavirus was 10-times higher than for seasonal influenza. Additional evidence, however, suggests the validity of this estimation could benefit from vetting for biases and miscalculations. The main objective of this article is to critically appraise the coronavirus mortality estimation presented to Congress. Informational texts from the World Health Organization and the Centers for Disease Control and Prevention are compared with coronavirus mortality calculations in Congressional testimony. Results of this critical appraisal reveal information bias and selection bias in coronavirus mortality overestimation, most likely caused by misclassifying an influenza infection fatality rate as a case fatality rate. Public health lessons learned for future infectious disease pandemics include: safeguarding against research biases that may underestimate or overestimate an associated risk of disease and mortality; reassessing the ethics of fear-based public health campaigns; and providing full public disclosure of adverse effects from severe mitigation measures to contain viral transmission.

Impact of matching error on linked mortality outcome in a data linkage of secondary mental health data with Hospital Episode Statistics (HES) and mortality records in South East London: a cross-sectional study.

OBJECTIVES: Linkage of electronic health records (EHRs) to Hospital Episode Statistics (HES)-Office for National Statistics (ONS) mortality data has provided compelling evidence for lower life expectancy in people with severe mental illness. However, linkage error may underestimate these estimates. Using a clinical sample (n=265 300) of individuals accessing mental health services, we examined potential biases introduced through missed matching and examined the impact on the association between clinical disorders and mortality. SETTING: The South London and Maudsley NHS Foundation Trust (SLaM) is a secondary mental healthcare provider in London. A deidentified version of SLaM's EHR was available via the Clinical Record Interactive Search system linked to HES-ONS mortality records. PARTICIPANTS: Records from SLaM for patients active between January 2006 and December 2016. OUTCOME MEASURES: Two sources of death data were available for SLaM participants: accurate and contemporaneous date of death via local batch tracing (gold standard) and date of death via linked HES-ONS mortality data. The effect of linkage error on mortality estimates was evaluated by comparing sociodemographic and clinical risk factor analyses using gold standard death data against HES-ONS mortality records. RESULTS: Of the total sample, 93.74% were successfully matched to HES-ONS records. We found a number of statistically significant administrative, sociodemographic and clinical differences between matched and unmatched records. Of note, schizophrenia diagnosis showed a significant association with higher mortality using gold standard data (OR 1.08; 95% CI 1.01 to 1.15; p=0.02) but not in HES-ONS data (OR 1.05; 95% CI 0.98 to 1.13; p=0.16). Otherwise, little change was found in the strength of associated risk factors and mortality after accounting for missed matching bias. CONCLUSIONS: Despite significant clinical and sociodemographic differences between matched and unmatched records, changes in mortality estimates were minimal. However, researchers and policy analysts using HES-ONS linked resources should be aware that administrative linkage processes can introduce error.

Multilevel Factor Score Regression.

Multilevel SEM is an increasingly popular technique to analyze data that are both hierarchical and contain latent variables. The parameters are usually jointly estimated using a maximum likelihood estimator (MLE). This has the disadvantage that a large sample size is needed and misspecifications in one part of the model may influence the whole model. We propose an alternative stepwise estimation method, which is an extension of the Croon method for factor score regression. In this article, we extend this method to the multilevel setting. A simulation study was used to compare this new estimation method to the standard MLE. The Croon method outperformed MLE with regard to convergence rate, bias, MSE, and coverage, in particular when models contained a structural misspecification. In conclusion, the Croon method seems to be a promising alternative to MLE.

Creating Misspecified Models in Moment Structure Analysis.

To understand how SEM methods perform in practice where models always have misfit, simulation studies often involve incorrect models. To create a wrong model, traditionally one specifies a perfect model first and then removes some paths. This approach becomes difficult or even impossible to implement in moment structure analysis and fails to control the amounts of misfit separately and precisely for the mean and covariance parts. Most importantly, this approach assumes a perfect model exists and wrong models can eventually be made perfect, whereas in practice models are all implausible if taken literally and at best provide approximations of the real world. To improve the traditional approach, we propose a more realistic and flexible way to create model misfit for multiple group moment structure analysis. Given (a) the model [Formula: see text] and [Formula: see text], (b) population model parameters [Formula: see text], and (c) [Formula: see text] and [Formula: see text] specified by the researcher, our method creates [Formula: see text] and [Formula: see text] to simultaneously satisfy (a) [Formula: see text], (b) the mean structure's misfit equals [Formula: see text], and (c) the covariance structure's misfit equals [Formula: see text].

Estimation of Needs for Addiction Services: A Youth Model.

OBJECTIVE: In the field of health care services, resource allocation is increasingly determined based on a population needs model. Although service needs models have been developed for adults with substance use problems, it would seem inappropriate to apply them indiscriminately to young people. METHOD: The method used proposes six steps: (1) targeting the population, (2) estimating the proportion of the population affected by substance misuse and (3) the proportion of youths who should receive services, (4) identifying categories of services, (5) estimating the proportions of youths who should have access to each category of services, and (6) applying the model to real use of services by youths to recalibrate it. RESULTS: Youths ages 12-17 from the Province of Québec were classified within a tiered model comprising four levels of substance use severity. Youths in need of services varied from 38% (weak response) to 95% (high response) for the highest severity cases. Service categories retained are detoxification/intoxication, outpatient, and residential, with each one being subdivided into four categories. The proportion of youths from each tier who should access categories and subcategories of services varied widely. After a pre-experimentation, the model was adjusted. CONCLUSIONS: The model can be applied in different jurisdictions, with the caution of adjusting prevalence to local reality. Further improvement will be based on more accurate information concerning the path of clients through services, better strategies to reach youths in need of services, and increased knowledge of optimal service categories. Models adapted to low- or moderate-income countries, where the health care system has minimal services in the areas of mental health and addiction, should be developed.

Development of a Needs-Based Planning Model to Estimate Required Capacity of a Substance Use Treatment System.

OBJECTIVE: Substance use services and supports have traditionally been funded without the benefit of a comprehensive, quantitative planning model closely aligned with population needs. This article describes the methodology used to develop and refine key features of such a model, gives an overview of the resulting Canadian prototype, and offers examples and lessons learned in pilot work. METHOD: The need for treatment was defined according to five categories of problem severity derived from national survey data and anticipated levels of help-seeking estimated from a narrative synthesis of international literature. A pan-Canadian Delphi procedure was used to allocate this help-seeking population across an agreed-upon set of treatment service categories, which included three levels each of withdrawal management, community, and residential treatment services. Projections of need and required service capacity for Canadian health planning regions were derived using synthetic estimation by age and gender. The model and gap analyses were piloted in nine regions. RESULTS: National distribution of need was estimated as Tier 1: 80.7%; Tier 2: 10.4%; Tier 3: 6.1%; Tier 4: 2.6%; and Tier 5: 0.2%. Pilot work of the full estimation protocol, including gap analysis, showed the results triangulated with other indicators of need and were useful for local planning. CONCLUSIONS: Lessons learned from pilot testing were identified, including challenges with the model itself and those associated with its implementation. The process of estimation developed in this Canadian prototype, and the specifics of the model itself, can be adapted to other jurisdictions and contexts.

Estimating the Needs of Substance Problem Use Services: An Exercise in Seven Finnish Municipalities Using Nationally Collected, Municipal-Level Survey and Register Data†.

OBJECTIVE: The needs of substance problem use services (SPUSs) should ideally be assessed locally to support the provision of appropriate, cost-effective services for the population. In this article we present a model for estimating the adult population's potential needs for and actual use of SPUSs. We used Finnish survey and register data as material for a qualitative assessment. The purpose of our article is to contribute to a discussion on the dimensions of assessment of the need for SPUSs at a local level. METHOD: Seven Finnish municipalities were chosen as examples. The need for SPUSs was assessed by freely available register and survey data of the use of services, substance use and problem use, side effects of use, and lack of social support. Babor et al.'s (2008) description of links between the use of services and need for treatment, in terms of substance use and general social conditions, and Ritter's (2014a) set of methods for assessing the need for treatment are used as theoretical background. RESULTS: The number of people using SPUSs varied from one municipality to the next. The local service system policy and the general well-being of the population have a remarkable role in the use of SPUSs. CONCLUSIONS: Estimations of need and demand with indicators can be useful for local treatment system policy but must be interpreted with thorough knowledge of the local treatment and social handling resources and general social situation. Comparisons between different local areas should be made with caution.

A Comparison of Pediatric Weight Estimation Methods for Emergency Resuscitation.

OBJECTIVES: Obtaining accurate pediatric weight is necessary during emergency resuscitation. Although several weight estimation methods exist, the most precise method has not been conclusively determined. This study aimed to evaluate the validity, reliability, and practicality of these tools. METHODS: A prospective observational study was conducted in healthy Thai children aged 6 months to 12 years. Correlations between estimated and actual weights were tested. Validity was assessed by mean bias (estimated weight minus actual weight) and accuracy (10% error). Practicality was evaluated by time usage and data derived from user questionnaires. RESULTS: Four hundred thirty participants with mean age of 6.7 years and mean weight of 26 kg were enrolled. A strong correlation between estimated weight and actual weight in all methods was demonstrated. Parental estimation was the most accurate tool in all age groups, with the lowest overall mean error (ME) of -0.83 kg and the highest accuracy of 88.7%. The Broselow tape was the second most accurate tool in ages younger than 1 year and 1-to-5-year age groups (ME = 0.23 and 0.50 kg; accuracy = 55.3% and 54.1%, respectively). The Mercy method was the second most accurate tool in the 6-to-10-year and 11-to-12-year age groups (ME = -2.47 and -2.77; accuracy = 54.6% and 67.9%, respectively). The Broselow tape had the highest score for practicality of use. CONCLUSIONS: Parental estimation was the most accurate method in every age group. The next best alternative is the Broselow tape in children aged 5 years or younger and the Mercy method in children aged older than 5 years.

A Generic Model for Follicular Lymphoma: Predicting Cost, Life Expectancy, and Quality-Adjusted-Life-Year Using UK Population-Based Observational Data.

OBJECTIVES: To use real-world data to develop a flexible generic decision model to predict cost, life expectancy, and quality-adjusted life-years (QALYs) for follicular lymphoma (FL) in the general patient population. METHODS: All patients newly diagnosed with FL in the UK's population-based Haematological Malignancy Research Network (www.hmrn.org) between 2004 and 2011 were followed until 2015 (N = 740). Treatment pathways, QALYs, and costs were incorporated into a discrete event simulation to reflect patient heterogeneity, including age and disease management. Two scenario analyses, based on the latest National Institute for Health and Clinical Excellence (NICE) guidelines (rituximab induction therapy for newly diagnosed asymptomatic patients and rituximab maintenance therapy for patients between treatments), were conducted and their economic impacts were compared to current practice. RESULTS: Incidence-based analysis revealed expected average lifetime costs ranging from £6,165 [US$7,709] to £63,864 [US$79,862] per patient, and average life expectancy from 75 days to 17.56 years. Prevalence-based analysis estimated average annual treatment costs of £60-65 million [US$75-80 million], accounting for approximately 10% of the United Kingdom's annual National Health Service budget for hematological cancers as a whole. Assuming that treatment effects reported in trials are applicable to all patient groups, scenario analyses for two recent NICE guidelines demonstrated potential annual cost savings for the United Kingdom that ranged with uptake frequency from £0.6 million to £11 million [US$0.75-2.75 million]. CONCLUSIONS: Costs, survival, and QALYs associated with FL vary markedly with patient characteristics and disease management. Allowing the production of more realistic outcomes across the patient population as a whole, our model addresses this heterogeneity and is a useful tool with which to evaluate new technologies/treatments to support healthcare decision makers.

Prevalence of asthma, aspirin sensitivity and allergy in chronic rhinosinusitis: data from the UK National Chronic Rhinosinusitis Epidemiology Study.

BACKGROUND: Chronic rhinosinusitis (CRS) is a common disorder associated with other respiratory tract diseases such as asthma and inhalant allergy. However, the prevalence of these co-morbidities varies considerably in the existing medical literature and by phenotype of CRS studied. The study objective was to identify the prevalence of asthma, inhalant allergy and aspirin sensitivity in CRS patients referred to secondary care and establish any differences between CRS phenotypes. METHODS: All participants were diagnosed in secondary care according to international guidelines and invited to complete a questionnaire including details of co-morbidities and allergies. Data were analysed for differences between controls and CRS participants and between phenotypes using chi-squared tests. RESULTS: The final analysis included 1470 study participants: 221 controls, 553 CRS without nasal polyps (CRSsNPs), 651 CRS with nasal polyps (CRSwNPs) and 45 allergic fungal rhinosinusitis (AFRS). The prevalence of asthma was 9.95, 21.16, 46.9 and 73.3% respectively. The prevalence of self-reported confirmed inhalant allergy was 13.1, 20.3, 31.0 and 33.3% respectively; house dust mite allergy was significantly higher in CRSwNPs (16%) compared to CRSsNPs (9%, p < 0.001). The prevalence of self- reported aspirin sensitivity was 2.26, 3.25, 9.61 and 40% respectively. The odds ratio for aspirin sensitivity amongst those with AFRS was 28.8 (CIs 9.9, 83.8) p < 0.001. CONCLUSIONS: The prevalence of asthma and allergy in CRS varies by phenoytype, with CRSwNPs and AFRS having a stronger association with both. Aspirin sensitivity has a highly significant association with AFRS. All of these comorbidities are significantly more prevalent than in non-CRS controls and strengthen the need for a more individualised approach to the combined airway.

The convergence analysis of SpikeProp algorithm with smoothing L1∕2 regularization.

Unlike the first and the second generation artificial neural networks, spiking neural networks (SNNs) model the human brain by incorporating not only synaptic state but also a temporal component into their operating model. However, their intrinsic properties require expensive computation during training. This paper presents a novel algorithm to SpikeProp for SNN by introducing smoothing L1∕2 regularization term into the error function. This algorithm makes the network structure sparse, with some smaller weights that can be eventually removed. Meanwhile, the convergence of this algorithm is proved under some reasonable conditions. The proposed algorithms have been tested for the convergence speed, the convergence rate and the generalization on the classical XOR-problem, Iris problem and Wisconsin Breast Cancer classification.