Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis

OBJECTIVE: To externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19. DESIGN: Two stage individual participant data meta-analysis. SETTING: Secondary and tertiary care. PARTICIPANTS: 46 914 patients across 18 countries, admitted to a hospital with polymerase chain reaction confirmed covid-19 from November 2019 to April 2021. DATA SOURCES: Multiple (clustered) cohorts in Brazil, Belgium, China, Czech Republic, Egypt, France, Iran, Israel, Italy, Mexico, Netherlands, Portugal, Russia, Saudi Arabia, Spain, Sweden, United Kingdom, and United States previously identified by a living systematic review of covid-19 prediction models published in The BMJ, and through PROSPERO, reference checking, and expert knowledge. MODEL SELECTION AND ELIGIBILITY CRITERIA: Prognostic models identified by the living systematic review and through contacting experts. A priori models were excluded that had a high risk of bias in the participant domain of PROBAST (prediction model study risk of bias assessment tool) or for which the applicability was deemed poor. METHODS: Eight prognostic models with diverse predictors were identified and validated. A two stage individual participant data meta-analysis was performed of the estimated model concordance (C) statistic, calibration slope, calibration-in-the-large, and observed to expected ratio (O:E) across the included clusters. MAIN OUTCOME MEASURES: 30 day mortality or in-hospital mortality. RESULTS: Datasets included 27 clusters from 18 different countries and contained data on 46 914patients. The pooled estimates ranged from 0.67 to 0.80 (C statistic), 0.22 to 1.22 (calibration slope), and 0.18 to 2.59 (O:E ratio) and were prone to substantial between study heterogeneity. The 4C Mortality Score by Knight et al (pooled C statistic 0.80, 95% confidence interval 0.75 to 0.84, 95% prediction interval 0.72 to 0.86) and clinical model by Wang et al (0.77, 0.73 to 0.80, 0.63 to 0.87) had the highest discriminative ability. On average, 29% fewer deaths were observed than predicted by the 4C Mortality Score (pooled O:E 0.71, 95% confidence interval 0.45 to 1.11, 95% prediction interval 0.21 to 2.39), 35% fewer than predicted by the Wang clinical model (0.65, 0.52 to 0.82, 0.23 to 1.89), and 4% fewer than predicted by Xie et al's model (0.96, 0.59 to 1.55, 0.21 to 4.28). CONCLUSION: The prognostic value of the included models varied greatly between the data sources. Although the Knight 4C Mortality Score and Wang clinical model appeared most promising, recalibration (intercept and slope updates) is needed before implementation in routine care

Paleontology of leaf beetles

`The rate of evolution in any large group is not uniform; there are periods of relatise stability, and periods of comparatively rapid change.' Cockerell and LeVeque, 1931 To Yenli Ych, my beloved wife, a most wonderful person! The fossil record of the Chrysomelidae can be tentatively traced back to the late Paleozoic to early Mesozoic Triassic. Mesozoic records at least 9 subfamilies, 19 genera, and 35 species, are represented by the Sagrinae, the exclusively Mesozoic Proto scelinae, Clytrinae, Cryptocephalinae, Eumolpinae, Chrysomelinae. Galerucinac, Alticinae, and Cassidinae. Cenozoic records at least 12 subfamilies- 63 % of the extant- 12! genera, and 325 species, include the same extant subfamilies as well as the Donaciinae, Zeugophorinae, Criocerinae, and Hispinae and can be frequently identified to genus, especially if preserved in amber. Quaternary records are often identified to extant species. tn total, at least t3! genera about 4 % of total extant, and 357 species < 1 % have been reported. At least, 24 genera <1 % of the extant seem to be extinct. Although reliable biological information associated with the fossil chrysomelids is very scarce, it seems that most of the modern host-plant associations were established, at least, in the late Mesozoic to early Cenozoic. As a whole, stasis seems to be the general rule of the chrysomelid fossil record. Together with other faunal elements, chrysomelids, especially donaciines, have been used as biogeographic and paleoclimatological indicators in the Holocene. I

Anthropometric, motor ability and physiological profiles of Indian national club footballers: a comparative study

Football is probably the most popular game worldwide but there is still limited scientific information available concerning the physique and performance qualities of elite Indian footballers. Team games are sports where size, shape, body composition and fitness all play an important part in providing distinct advantages for specific playing positions. Hence an attempt has been made to stud y the various anthropometric parameters, motor ability and physiological profiles of the different Indian national club footballers and also to compare the above parameters with their international counterparts. The present study was carried out on one hundred fifty (150) male Indian footballers of six different national clubs of India including three from Kolkata (East Bengal, Mohan Bagan & Mohammedan Sporting) and other three from Goanese clubs (Salgaokar, Vasco & Dempo). The players were also sub-divided according to their specific field positions. Physical and physiological profiles including height, weight, percentage body fat (%BF), flexibility, agility, explosive power, and VO2 max were measured by standard procedures. It was noted that the mean values of age, height, weight and %BF were significantly different among footballers of different national clubs. Among the motor ability andphysiological qualities only flexibility, agility and VO2 max were significantly different among the footballers of different national clubs (

Probabilistic Resource Analysis by Program Transformation

The aim of a probabilistic resource analysis is to derive a probability distribution of possible resource usage for a program from a probability distribution of its input. We present an automated multi- phase rewriting based method to analyze programs written in a subset of C. It generates a probability distribution of the resource usage as a possibly uncomputable expression and then transforms it into a closed form expression using over-approximations. We present the technique, outline the implementation and show results from experiments with the system