EditorialPrimer on Reporting Statistics: Kayaks and Walking Trees Once MoreDouglas Curran-EverettDouglas Curran-Everett* Corresponding Author; email: [email protected]Biostatistics and Bioinformatics, M222, National Jewish Health, Denver, Colorado, United States*Published Online:16 Nov 2023https://doi.org/10.1152/advan.00231.2023MoreSectionsPDF (390 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInWeChat AbstractN/A Back to Top Next Download PDF FiguresReferencesRelatedInformation More from this issue > Articles in PressPublished 11/16/23 1:00 AM Crossmark Copyright & PermissionsCopyright © 2023, Advances in Physiology Educationhttps://doi.org/10.1152/advan.00231.2023PubMed37969076History Received 26 October 2023 Accepted 10 November 2023 Published online 16 November 2023 Keywordsstatisticsguidelines Metrics

OBJECTIVES: Prior work has shown substantial between-hospital variation in do-not-resuscitate orders, but stability of do-not-resuscitate preferences between hospitalizations and the institutional influence on do-not-resuscitate reversals are unclear. We determined the extent of do-not-resuscitate reversals between hospitalizations and the association of the readmission hospital with do-not-resuscitate reversal. DESIGN: Retrospective cohort study. SETTING: California Patient Discharge Database, 2016-2018. PATIENTS: Nonsurgical patients admitted to an acute care hospital with an early do-not-resuscitate order (within 24 hr of admission). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We identified nonsurgical adult patients who survived an initial hospitalization with an early-do-not-resuscitate order and were readmitted within 30 days. The primary outcome was the association of do-not-resuscitate reversal with readmission to the same or different hospital from the initial hospital. Secondary outcomes included association of readmission to a low versus high do-not-resuscitate-rate hospital with do-not-resuscitate reversal. Among 49,336 patients readmitted within 30 days following a first do-not-resuscitate hospitalization, 22,251 (45.1%) experienced do-not-resuscitate reversal upon readmission. Patients readmitted to a different hospital versus the same hospital were at higher risk of do-not-resuscitate reversal (59.5% vs 38.5%; p < 0.001; adjusted odds ratio = 2.4; 95% CI, 2.3-2.5). Patients readmitted to low versus high do-not-resuscitate-rate hospitals were more likely to have do-not-resuscitate reversals (do-not-resuscitate-rate quartile 1 77.0% vs quartile 4 27.2%; p < 0.001; adjusted odds ratio = 11.9; 95% CI, 10.7-13.2). When readmitted to a different versus the same hospital, patients with do-not-resuscitate reversal had higher rates of mechanical ventilation (adjusted odds ratio = 1.9; 95% CI, 1.6-2.1) and hospital death (adjusted odds ratio = 1.2; 95% CI, 1.1-1.3). CONCLUSIONS: Do-not-resuscitate reversals at the time of readmission are more common than previously reported. Although changes in patient preferences may partially explain between-hospital differences, we observed a strong hospital effect contributing to high do-not-resuscitate-reversal rates with significant implications for patient outcomes and resource.

RATIONALE:Asthma severity in children generally starts mild but may progress and stay severe for unknown reasons.OBJECTIVES:Identify factors in childhood that predict persistence of severe asthma in late adolescence and early adulthood.METHODS:The Childhood Asthma Management Program is the largest and longest asthma trial in 1041 children aged 5-12 years with mild to moderate asthma. We evaluated 682 participants from the program with analyzable data in late adolescence (age 17-19) and early adulthood (age 21-23).MEASUREMENTS:Severe asthma was defined using criteria from the American Thoracic Society and the National Asthma Education and Prevention Program to best capture severe asthma. Logistic regression with stepwise elimination was used to analyze clinical features, biomarkers, and lung function predictive of persistence of severe asthma.MAIN RESULTS:In late adolescence and early adulthood 12% and 19% of the patents had severe asthma, respectively; only 6% were severe at both time periods. For every 5% decrease in post bronchodilator FEV1/FVC in childhood, the odds of persistence of severe asthma increased 2.36-fold (95% CI: 1.70-3.28; p <0.0001), for participants with maternal smoking during pregnancy odds of persistence of severe asthma increased 3.17-fold (95% CI: 1.18-8.53, p=0.02). Reduced growth lung function trajectory was significantly associated with persistence of severe asthma compared to normal growth.CONCLUSIONS:Lung function and maternal smoking during pregnancy were significant predictors of severe asthma from late adolescence to early adulthood. Interventions to preserve lung function early may prevent disease progression.

Biofilm-associated infections in orthopedic surgery lead to worse clinical outcomes and greater morbidity and mortality. The scope of the problem encompasses infected total joints, internally fixed fractures, and implanted devices. Diagnosis is difficult. Cultures are often negative, and antibiotic treatments are ineffective. The infections resist killing by the immune system and antibiotics. The organized matrix structure of extracellular polymeric substances within the biofilm shields and protects the bacteria from identification and immune cell action. Bacteria in biofilms actively modulate their redox environment and can enhance the matrix structure by creating an oxidizing environment. We postulated that a potent redox-active metalloporphyrin MnTE-2-PyP (chemical name: manganese (II) meso-tetrakis-(N-methylpyridinium-2-yl) porphyrin) that scavenges reactive species and modulates the redox state to a reduced state, would improve the effect of antibiotic treatment for a biofilm-associated infection. An infected fracture model with a midshaft femoral osteotomy was created in C57B6 mice, internally fixed with an intramedullary 23-gauge needle and seeded with a biofilm-forming variant of Staphylococcus aureus. Animals were divided into three treatment groups: control, antibiotic alone, and combined antibioticplus MnTE-2-PyP. The combined treatment group had significantly decreased bacterial counts in harvested bone, compared with antibiotic alone. In vitro crystal violet assay of biofilm structure and corresponding nitroblue tetrazolium assay for reactive oxygen species (ROS) demonstrated that MnTE-2-PyP decreased the biofilm structure and reduced ROS in a correlated and dose-dependent manner. The biofilm structure is redox-sensitive in S. aureus and an ROS scavenger improved the effect of antibiotic therapy in model of biofilm-associated infections.

EditorialEvolution in statistics: P values, statistical significance, kayaks, and walking treesDouglas Curran-EverettDouglas Curran-EverettDivision of Biostatistics and Bioinformatics, National Jewish Health, Denver, ColoradoDepartment of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Denver, ColoradoPublished Online:15 May 2020https://doi.org/10.1152/advan.00054.2020MoreSectionsPDF (138 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInWeChat The scientific literature is littered with the adjective significant and the descriptive phrase statistically significant. Established members of the statistical community now recommend that a scientific paper simply report an actual P value divorced from any word or phrase that reflects statistical significance (Hurlbert SH, Levine RA, Utts J. Am Stat 73, Suppl 1: 352–357, 2019). In this editorial, I illustrate why this deceptively simple change is important.A Brief History of Hypothesis Tests, P Values, and SignificanceEarly hypothesis tests, from the Trial of the Pyx in 1279 through the assessment of a discrepant celestial measurement in the 1700s, mandated a binary outcome: the measurement of weight or position either was or was not within some allowable deviation (see Ref. 6).Between the 1800s and the early 1900s, the focus of a hypothesis test shifted from whether the measurement of a coin or a star was within an allowable deviation to whether some event in mathematics or science could be attributed to chance alone, but the outcome of that hypothesis test remained a binary one (see Ref. 6). If some event—if some difference—was unlikely to have resulted from chance alone, then Edgeworth described that difference as significant, very significant, or as significant and not accidental (15). Thirty-five years later, Boring (2) warned about interpreting a significant difference in the absence of scientific context.In his pivotal Statistical Methods for Research Workers (16), Sir Ronald Fisher also used significant when he discussed the magnitude of a deviation he regarded as beyond chance, and he defined 0.05 as the benchmark for when he considered some deviation as significant:The value for which P = ·05, or 1 in 20, is 1·96 or nearly 2; it is convenient to take this point as a limit in judging whether a deviation is to be considered significant or not. Deviations exceeding twice the standard deviation are thus formally regarded as significant.In the intervening 100 yr, Fisher’s significance level of 0.05 assumed mythic proportions despite subsequent but less visible elaborations by Fisher himself:If one in twenty does not seem high enough odds, we may, if we prefer it, draw the line at one in fifty (the 2 per cent. point), or one in a hundred (the 1 per cent. point). Personally, the writer prefers to set a low standard of significance at the 5 per cent. point, and ignore entirely all results which fail to reach this level. A scientific fact should be regarded as experimentally established only if a properly designed experiment rarely fails to give this level of significance.[Ref. 17 (1926)]The attempts that have been made to explain the cogency of tests of significance in scientific research . . . seem to miss the essential nature of such tests. A [person] who “rejects” a hypothesis provisionally, as a matter of habitual practice, when the significance is at the 1% level or higher, will certainly be mistaken in not more than 1% of such decisions. For when the hypothesis is correct he will be mistaken in just 1% of these cases, and when it is incorrect he will never be mistaken in rejection. . .. However, the calculation is absurdly academic, for in fact no scientific worker has a fixed level of significance at which from year to year, and in all circumstances, he rejects hypotheses; he rather gives his mind to each particular case in the light of his evidence and his ideas. Further, the calculation is based solely on a hypothesis, which, in the light of the evidence, is often not believed to be true at all, so that the actual probability of erroneous decision, supposing such a phrase to have any meaning, may be much less than the frequency specifying the level of significance.[Ref. 18 (1956)]Recent suggestions that the critical significance level α—the benchmark for how statistically unusual a result must be before we reject the corresponding null hypothesis—be set at 0.005 or 0.001 (23, 25) minimize the chance that we get a false positive and improve reproducibility (see Ref. 10), but a lower, more stringent significance level α, by itself, fails to address the binary nature of the benchmark (1).Suppose we define beforehand α = 0.05 and then obtain, using actual data, P = 0.051. Is our scientific conclusion going to differ from our conclusion had P = 0.049? I hope not. The same logic applies had we defined α = 0.001: does 0.0011 truly differ from 0.0009? No.One strategy that circumvents this problem with a binary benchmark1 is to report the actual P value rather than simply P < 0.05 or P > 0.05 (see Refs. 12 and 14). This strategy, however, has met with limited success in journals published by the American Physiological Society (APS): guidelines for reporting statistics (12) had virtually no impact on the occurrence of actual P values (11, 13); see Table 1.Table 1. American Physiological Society journal manuscripts in 2003 and 2006: reporting of statisticsnP Value2003200620032006Am J Physiol Cell Physiology303221313 Endocrinol Metab283023930 Gastrointest Liver Physiol282721417 Heart Circ Physiol626271920 Lung Cell Mol Physiol262611918 Regul Integr Comp Physiol293844127 Renal Physiol25289417*J Appl Physiol575192634J Neurophysiol616993038For each journal, values are n, the no. of manuscripts reviewed, and the percentage of manuscripts that reported actual P values (for example, P = 0.02 rather than P < 0.05 or P = 0.11 rather than P > 0.05). From August 2003 through July 2004, these journals published a total of roughly 3,500 original articles; the number of articles reviewed represents a 10% sample (systematic random sampling, fixed start) of the original articles published by each journal. From August 2005 through July 2006, these journals published a total of 3,675 original articles; the number of articles reviewed represents a complete survey of the original articles published by each journal.*Italicized pair indicates the percentage in 2003 was less than the percentage in 2006 (P = 0.06, exact binomial test, 1-tailed). [Adapted from Table 1 in Ref. 13.]Although the P value associated with the statistical test of some null hypothesis is useful—it helps guard against an unwarranted conclusion, or it helps argue for a real experimental effect (3, 24)—the only question it can answer is a trivial one: is there anything other than random variation going on here? Statisticians have long warned against a sole focus on hypothesis tests and their resultant P values (see Refs. 6 and 14).The Vagaries of P ValuesAs a thought experiment I have posed before (see Refs. 9 and 10), suppose we want to learn if some intervention affects the biological thing we care about. If we use two groups—a control group and an experimental group—we might ask if our samples came from the same or different populations. Therefore, the null and alternative hypotheses, H0 and H1, are:H0: The samples come from the same population.H1: The samples come from different populations.If we want to know whether the populations have the same mean, we can write these asH0:Δμ=0H1:Δμ≠0,where Δμ, the difference in population means, is the difference between the means of the experimental and control populations.We know from previous simulations in which the null hypothesis H0: Δμ = 0 is true (see Refs. 9 and 10) that the observed P values that result from the test of this null hypothesis are distributed over a wide range of values (Fig. 1): only 100α% of them will be smaller than the critical significance level α.Fig. 1.The distribution of observed P values from 100,000 replications of a simulation in which the null hypothesis, H0: Δμ = 0, is true. We drew at random two samples, each with n observations, from a standard normal distribution (top left; see Refs. 4 and 6), did a two-sample t test (using a critical significance level α = 0.05), and then repeated this process to generate a total of 100,000 replications. In 5% of the replications (gray), P < 0.05: that is, we reject a true null hypothesis (see Ref. 6). The proportion of false positives depends solely on the critical significance level α. [From Ref. 9, Fig. 1.]Download figureDownload PowerPointWhat may be less obvious is that the observed P values from the simulated test of a false null hypothesis (Fig. 2) are distributed also over a wide range of values (9, 10, 21); see Fig. 3 and Table 2. In this situation, it is the power of the statistical test (see Ref. 7) that determines the proportion of observed P values that will be smaller—and larger—than the critical significance level α.Fig. 2.The populations. Population 0 is a standard normal distribution with mean μ0 = 0 and standard deviation σ0 = 1. Population 1 is a normal distribution with mean μ1 = 1 and standard deviation σ1 = 1. Therefore, the true difference in population means, Δμ, is μ1 − μ0 = 1, and the effect size is Δμ/σ = 1.Download figureDownload PowerPointFig. 3.The distributions of observed P values from 100,000 replications of simulations in which the null hypothesis, H0: Δμ = 0, is false. We drew at random two samples, each with 10 (top) or 23 (bottom) observations, from the normal distributions in Fig. 2, did a two-sample t test (using a critical significance level α = 0.05), and then repeated this process to generate a total of 100,000 replications. The proportion of replications in which P was less than 0.0001, 0.001, 0.01, or 0.05 is listed in the upper portion of each graph.Download figureDownload PowerPointTable 2. Percentiles of observed P values when the null hypothesis is falsePercentilenPower2.5255056759197.5100.560.00020.0070.040.050.130.370.72230.91<0.00010.00010.0010.0020.010.050.16Values are n, the no. of observations drawn from each population in Fig. 2; theoretical power of the 2-sample t test used to evaluate the null hypothesis H0: Δμ = 0; and percentiles of the distributions of observed P values depicted in Fig. 3. When power is 0.91, 9% of the observed P values are greater than α = 0.05.The dichotomy between statistical significance and scientific importance can be illustrated by taking progressively larger numbers of observations from each population in Fig. 2 (Table 3): statistical significance increases (P decreases), but the scientific importance as estimated by Δy¯, the difference between sample means, remains constant.Table 3. Limitations of statistical significancenΔy¯SE {Δy¯}dftP95% CICI Width210.53121.8830.20−1.28 to +3.284.56410.29463.3990.010.28 to 1.721.44810.528141.8940.08−0.13 to +2.132.261010.364182.7450.010.23 to 1.771.541510.384282.6040.010.21 to 1.791.582010.396382.5230.020.20 to 1.801.602310.282443.5500.00090.43 to 1.571.142510.239484.1800.00010.52 to 1.480.963210.220624.5430.000030.56 to 1.440.88Values are n, the no. of observations drawn from each population in Fig. 2; Δy¯, the difference between sample means; SE {Δy¯}, the standard error of the difference between sample means; df, degrees of freedom; t, the test statistic used to evaluate the null hypothesis, H0: Δμ = 0; P, the probability associated with t and the corresponding df; 95% CI, 95% confidence interval for Δμ, the difference between population means; CI Width, width of the 95% CI for Δμ. The difference Δy¯ and the corresponding 95% CI for Δμ, the difference between population means, reflect the magnitude and uncertainty of the observed results. The test statistic t and its associated P value reflect statistical significance. As the number of observations drawn from each population increases, Δy¯, the estimated difference between population means, remains constant by virtue of the sampling process, but SE {Δy¯} decreases. As a consequence, the statistical significance increases (irregularly, because of random sampling), the scientific impact as estimated by Δy¯ remains constant, and precision of the scientific impact as estimated by the width of the confidence interval increases. See Refs. 8 and 14 for additional detail about the 2-sample t test used in this simulation. [Adapted from Ref. 14; the sampling process is depicted in Fig. 5 of Ref. 14.]Evolving Beyond Statistical SignificanceIn 2016 the American Statistical Association (ASA) issued a position statement that discussed P values and the notion of statistical significance (27). In October 2017 the ASA held a 2-day symposium on statistical inference that resulted in a 43-paper volume of The American Statistician (26). One of the papers (Ref. 22) advocated that a scientific paper simply report an actual P value divorced from phrases that reflect statistical significance. In their editorial preface (26), the Editors endorsed this recommendation:A label of statistical significance adds nothing to what is already conveyed by the value of p . . ..This deceptively simple change affords benefits beyond what you might imagine. Dropping the word significant or the phrase statistically significant prevents a reflexive association of scientific importance with a mere statistical result. This is helpful for two reasons. First, it is quite possible to have a statistically convincing change that is of little or no scientific relevance (see Ref. 5). And second, a binary distinction between not significant and statistically significant can be associated with a trivial difference in the magnitude of the underlying estimate of some effect (20).To appreciate how straightforward this change is to implement, consider this portion of the abstract from a March 2020 APS Select paper (28):Male, but not female, collecting duct Bmal1 knockout (CDBmal1KO) mice had significantly lower 24-h mean arterial pressure (MAP) than flox controls (105 ± 2 vs. 112 ± 3 mmHg for male mice and 106 ± 1 vs. 108 ± 1 mmHg for female mice, by telemetry). After 6 days on a high-salt (4% NaCl) diet, MAP remained significantly lower in male CDBmal1KO mice than in male flox control mice (107 ± 2 vs. 113 ± 1 mmHg), with no significant differences between genotypes in female mice (108 ± 2 vs. 109 ± 1 mmHg). . . . . However, MAP remained lower in male CDBmal1KO mice than in male flox control mice (124 ± 2 vs. 130 ± 2 mmHg).This is how that portion of the abstract could have been written2 to align with the recommendation of Refs. 22 and 26:Male, but not female, collecting duct Bmal1 knockout (CDBmal1KO) mice had lower 24-h mean arterial pressure (MAP) than flox controls [105 vs. 112 mmHg for male mice (P = 0.00) and 106 vs. 108 mmHg for female mice (P = 0.00), by telemetry]. After 6 days on a high-salt (4% NaCl) diet, MAP remained lower in male CDBmal1KO mice than in male flox control mice [107 vs. 113 mmHg (P = 0.00)], with no differences between genotypes in female mice [108 vs. 109 mmHg (P = 0.00)]. . . . However, MAP remained lower in male CDBmal1KO mice than in male flox control mice [124 vs. 130 mmHg (P = 0.00)].Reference 22 gives more examples.In April 2019, Hurlbert, Levine, and Utts, the authors of Ref. 22, contacted the Editors-in-Chief of the journals published by the APS. In their e-mail they encouraged the Editors to purge the phrase statistically significant from the APS journals’ future papers, and they argued that APS could spearhead this reform. The APS Publications Committee tabled discussion of this recommendation.In 1991 Ralph Fletcher wrote Walking Trees, a memoir of his experiences helping teachers in New York City schools learn how to teach writing (19). The title stems from a story Heather, a first-grader, wrote about a family trip to Florida:See, me and my mommy and daddy went to Florida, and we saw the walking trees down there. They walk with their roots. They take one step in a hundred years. [Significant pause.] We didn’t see them walk while we were there.For Fletcher, Heather’s trees became a metaphor for the terribly glacial rate of change in education and the Herculean effort required to make even the smallest progress. In my 2017 editorial (11), I wrote that trying to change the reporting practices of statistics was like trying to change the direction of an ocean liner with a kayak. Whether the metaphor is kayaks or walking trees, so it is with the use of statistics within science.In my 2017 editorial, I also announced that I had asked the Associate Editors and Editorial Board of Advances to actively promote two of the 2004 guidelines for reporting statistics (12). I wrote that I understood it was difficult to change entrenched practices and that I understood change was slow, but that did not mean we should not try. When the mainstream statistical community—virtually en masse—recommends a simple, specific course of action—report an actual P value without phrases that reflect statistical significance—the scientific community would do well to heed that recommendation.DISCLOSURESNo conflicts of interest, financial or otherwise, are declared by the author.AUTHOR CONTRIBUTIONSD.C.-E. analyzed data; prepared figures; drafted manuscript; edited and revised manuscript; approved final version of manuscript.ACKNOWLEDGMENTSI thank Ronald Wasserstein (Executive Director, American Statistical Association) for his time and for his helpful comments and suggestions.REFERENCES1. Amrhein V, Greenland S, McShane B. Scientists rise up against statistical significance. Nature 567: 305–307, 2019. doi:10.1038/d41586-019-00857-9. Crossref | PubMed | ISI | Google Scholar2. Boring EG. Mathematical vs. scientific significance. Psychol Bull 16: 335–338, 1919. doi:10.1037/h0074554.Crossref | Google Scholar3. Cox DR. 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Am Stat 73, Suppl 1: 1–19, 2019. doi:10.1080/00031305.2019.1583913.Crossref | ISI | Google Scholar27. Wasserstein RL, Lazar NA. The ASA’s statement on p-values: context, process, and purpose. Am Stat 70: 129–133, 2016. doi:10.1080/00031305.2016.1154108.Crossref | ISI | Google Scholar28. Zhang D, Jin C, Obi IE, Rhoads MK, Soliman RH, Sedaka RS, Allan JM, Tao B, Speed JS, Pollock JS, Pollock DM. Loss of circadian gene Bmal1 in the collecting duct lowers blood pressure in male, but not female, mice. Am J Physiol Renal Physiol 318: F710–F719, 2020. doi:10.1152/ajprenal.00364.2019. Link | ISI | Google ScholarFOOTNOTES1In contrast to the limitations of a binary benchmark in statistics, a binary decision in medicine is typically essential: a surgeon will either operate or not, or an oncologist will either prescribe chemotherapy or not. There may be uncertainty associated with the decision—will there be a good result?—but a clinical decision must be made.2In this revision I have deleted the standard error associated with each mean pressure (11, 12). Because Ref. 28 did not report actual P values, I have used P = 0.00 as a placeholder.AUTHOR NOTESAddress for reprint requests and other correspondence: D. Curran-Everett, Div. of Biostatistics and Bioinformatics, M222 National Jewish Health, 1400 Jackson St., Denver, CO 80206–2761 (e-mail: [email protected]org). Download PDF Back to Top Next FiguresReferencesRelatedInformation CollectionsAdvances in Physiology Education CollectionsReporting StatisticsStatistics Cited BySix months of unsupervised exercise training lowers blood pressure during moderate, but not vigorous, aerobic exercise in adults with well-healed burn injuriesJoseph C. Watso, Steven A. Romero, Gilbert Moralez, Mu Huang, Matthew N. Cramer, Elias Johnson, and Craig G. Crandall12 September 2022 | Journal of Applied Physiology, Vol. 133, No. 3Low-dose morphine reduces tolerance to central hypovolemia in healthy adults without affecting muscle sympathetic outflowJoseph C. Watso, Luke N. Belval, Frank A. Cimino, Bonnie D. Orth, Joseph M. Hendrix, Mu Huang, Elias Johnson, Josh Foster, Carmen Hinojosa-Laborde, and Craig G. Crandall7 June 2022 | American Journal of Physiology-Heart and Circulatory Physiology, Vol. 323, No. 1Low-dose morphine reduces pain perception and blood pressure, but not muscle sympathetic outflow, responses during the cold pressor testJoseph C. Watso, Luke N. Belval, Frank A. Cimino, Bonnie D. Orth, Joseph M. Hendrix, Mu Huang, Elias Johnson, Josh Foster, Carmen Hinojosa-Laborde, and Craig G. Crandall7 July 2022 | American Journal of Physiology-Heart and Circulatory Physiology, Vol. 323, No. 1Low-dose fentanyl reduces pain perception, muscle sympathetic nerve activity responses, and blood pressure responses during the cold pressor testJoseph C. Watso,* Mu Huang,* Luke N. Belval, Frank A. Cimino, Caitlin P. Jarrard, Joseph M. Hendrix, Carmen Hinojosa-Laborde, and Craig G. Crandall31 December 2021 | American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, Vol. 322, No. 1Skeletal myopathy in a rat model of postmenopausal heart failure with preserved ejection fractionRachel C. Kelley, Lauren Betancourt, Andrea M. Noriega, Suzanne C. Brinson, Nuria Curbelo-Bermudez, Dongwoo Hahn, Ravi A. Kumar, Eliza Balazic, Derek R. Muscato, Terence E. Ryan, Robbert J. van der Pijl, Shengyi Shen, Coen A. C. Ottenheijm, and Leonardo F. Ferreira7 January 2022 | Journal of Applied Physiology, Vol. 132, No. 1The impact of acute central hypovolemia on cerebral hemodynamics: does sex matter?Alexander J. Rosenberg, Victoria L. Kay, Garen K. Anderson, My-Loan Luu, Haley J. Barnes, Justin D. Sprick, Hannah B. Alvarado, and Caroline A. Rickards14 June 2021 | Journal of Applied Physiology, Vol. 130, No. 6A comparison of protocols for simulating hemorrhage in humans: step versus ramp lower body negative pressureAlexander J. Rosenberg, Victoria L. Kay, Garen K. Anderson, Justin D. Sprick, and Caroline A. Rickards18 February 2021 | Journal of Applied Physiology, Vol. 130, No. 2 More from this issue > Volume 44Issue 2June 2020Pages 221-224 Copyright & PermissionsCopyright © 2020 the American Physiological Societyhttps://doi.org/10.1152/advan.00054.2020PubMed32412384History Received 19 March 2020 Accepted 7 April 2020 Published online 15 May 2020 Published in print 1 June 2020 Metrics

Most children start out with mild asthma but some may progress to have severe asthma. It is important to identify factors in childhood that might predict severe asthma in late adolescence through early adulthood. The Childhood Asthma Management Program (CAMP) is the largest and longest asthma clinical trial in 1041 children aged 5-12 years with mild to moderate asthma. We evaluated 682 CAMP participants who had analyzable data in late adolescence (at age 17-19) through early adulthood (age 21-23). Multinomial logistic regression analysis with backwards elimination was used to investigate clinical features, biomarkers, and lung function predictive of severe asthma (based on National Asthma Education and Prevention Program criteria). 34.8% and 22.1% had severe asthma in late adolescence and early adulthood, respectively; only 11% of the total 682 patients stayed severe. Pre-bronchodilator FEV1/FVC ratio at baseline and duration of asthma were associated with persistence of severe asthma from late adolescence to early adulthood. For every 5 point increase in FEV1/FVC ratio at baseline, the odds of a person remaining severe could be as low as 33% (change of 0.05, p=0.0008, odds ratio 0.67 to 0.90). For every year increase of asthma duration, the odds of a person remaining severe could be as high as 18%. (change of 1 year, p= 0.10, odds ratio 0.98-1.18). Lung function was the only significant childhood predictor of severe asthma from late adolescence through early adulthood. Interventions to preserve lung function early in childhood should be developed to prevent and reverse disease progression.

Individuals born in the fall (September through November) are known to be at increased risk of food allergy (FA) and atopic dermatitis (AD). This association has been seen in various demographic groups, and a recent meta-analysis examined this phenomenon.1 The sequential progression of different allergic conditions is defined as the atopic march; in general, it begins with AD and progresses to FA, asthma, and allergic rhinitis (AR), suggesting that AD is an entry point for subsequent allergic diseases.

BackgroundNeurovascular insult, nonunion, and iatrogenic rotator cuff injury are concerns when using an intramedullary nail (IMN) for proximal humerus fracture. The purpose of this study was to identify a reproducible starting point and intraoperative imaging for nail insertion optimizing nail depth, tuberosity screw position, and protecting the axillary nerve and rotator cuff insertion. Our hypothesis was that a more medialized starting point would protect soft tissue structures and improve locking screw positioning.MethodsTen fresh-frozen cadavers underwent antegrade IMN with Grashey and modified lateral “precipice” view imaging. A guidewire was positioned medial to the coracoacromial ligament (CAL) in 5 cadavers and lateral to the CAL in 5. Distances from the nail entry point to anatomic landmarks were measured. Anatomic and histologic evaluations were performed, characterizing the nail perforation zone. Radiographs were compared between groups.ResultsThe medial CAL group had a greater distance of screw fixation to the axillary nerve, a shorter distance of greater tuberosity (GT) screw fixation at the rotator cuff insertion on the infraspinatus and teres minor tubercles, and greater screw spread with improved lesser tuberosity capture. Two laterally placed implants violated the rotator cuff tendon. Imaging demonstrated that the ideal starting pin position was medial to the articular margin at a distance equal to the width of the rotator cuff insertion footprint.ConclusionsMedial placement optimized fixation of the GT, avoided violation of the rotator cuff tendon and footprint, and was associated with an increased distance of proximal locking screw to the axillary nerve.

Background Lung cancer screening (LCS) via chest computed tomography (CT) scans can save lives by identifying early-stage tumors. However, most smokers die of comorbid smoking-related diseases. LCS scans contain information about smoking-related conditions that is not currently systematically assessed. Identifying these common comorbid diseases on CT could increase the value of screening with minimal impact on LCS programs. We determined the prevalence of 3 comorbid diseases from LCS eligible scans and quantified related adverse outcomes. Methods We studied COPD Genetic Epidemiology study (COPDGene®) participants (n=4078) who met criteria for LCS screening at enrollment (age > 55 years, and < 80 years, > 30 pack years smoking, current smoker or former smoker within 15 years of smoking cessation). CT scans were assessed for coronary artery calcification (CAC), emphysema, and vertebral bone density. We tracked the following clinically significant events: myocardial infarctions (MIs), strokes, pneumonia, respiratory exacerbations, and hip and vertebral fractures. Results Overall, 77% of eligible CT scans had one or more of these diagnoses identified. CAC (> 100 mg) was identified in 51% of scans, emphysema in 44%, and osteoporosis in 54%. Adverse events related to the underlying smoking-related diseases were common, with 50% of participants reporting at least one. New diagnoses of cardiovascular disease, emphysema and osteoporosis were made in 25%, 7% and 46%, of participants respectively. New diagnosis of disease was associated with significantly more adverse events than in participants who did not have CT diagnoses for both osteoporosis and cardiovascular risk. Conclusions Expanded analysis of LCS CT scans identified individuals with evidence of previously undiagnosed cardiovascular disease, emphysema or osteoporosis that corresponded with adverse events. LCS CT scans can potentially facilitate diagnoses of these smoking-related diseases and provide an opportunity for treatment or prevention.

BackgroundPrevious attempts to explore the heterogeneity of chronic obstructive pulmonary disease (COPD) clustered individual patients using clinical, demographic, and disease features. We developed continuous multidimensional disease axes based on radiographic and spirometric variables that split into an airway-predominant axis and an emphysema-predominant axis.MethodsThe COPD Genetic Epidemiology study (COPDGene®) is a cohort of current and former smokers, > 45 years, with at least 10 pack years of smoking history. Spirometry measures, blood pressure and body mass were directly measured. Mortality was assessed through continuing longitudinal follow-up and cause of death was adjudicated. Among 8157 COPDGene® participants with complete spirometry and computed tomography (CT) measures, the top 2 deciles of the airway-predominant and emphysema-predominant axes previously identified were used to categorize individuals into 3 groups having the highest risk for mortality using Cox proportional hazard ratios. These groups were also assessed for causal mortality. Biomarkers of COPD (fibrinogen, soluble receptor for advanced glycation end products [sRAGE], C-reactive protein [CRP], clara cell secretory protein [CC16], surfactant-D [SP-D]) were compared by group.FindingsHigh-risk subtype classification was defined for 2638 COPDGene® participants who were in the highest 2 deciles of either the airway-predominant and/or emphysema-predominant axis (32% of the cohort). These high-risk participants fell into 3 groups: airway-predominant disease only (APD-only), emphysema-predominant disease only (EPD-only) and combined APD-EPD. There was 26% mortality for the APD-only group, 21% mortality for the EPD-only group, and 54% mortality for the combined APD-EPD group. The APD-only group (n=1007) was younger, had a lower forced expiratory volume in 1 second (FEV1) percent (%) predicted and a strong association with the preserved ratio-impaired spirometry (PRISm) quadrant. The EPD-only group (n=1006) showed a relatively higher FEV1 % predicted and included largely GOLD stage 0, 1 and 2 partipants. Individuals in each of the 3 high-risk groups were at greater risk for respiratory mortality, while those in the APD-only group were additionally at greater risk for cardiovascular mortality. Biomarker analysis demonstrated a significant association of the APD-only group with CRP, and sRAGE demonstrated greatest significance with both the EPD-only and the combined APD-EPD groups.InterpretationAmong current and former smokers, individuals in the highest 2 deciles for mortality risk on the airway-predominant axis and the emphysema-predominant axis have unique associations to spirometric patterns, different imaging characteristics, biomarkers and causal mortality.

Background: Symptomatic burden guides clinical management in COPD, but its impact on long-term outcomes is unclear. Aim: Test for association between impaired health status and both mortality and lung function decline in smokers. Methods: We included 8,919 current or former smokers: 4,409 with no airflow obstruction (NAO), 2,727 with GOLD 1-2 COPD and 1,783 with GOLD 3-4 COPD. Repeat spirometry was obtained for 4,949 participants after 5 years. We tested associations between baseline SGRQ score ≥ 25 and both 5-year all-cause mortality and lung function decline using Cox and linear mixed models adjusted for age, sex, race, BMI, smoking status, smoking pack-years and baseline FEV1. Results: At baseline, 26%, 50% and 90% of participants with NAO, GOLD 1-2 and GOLD 3-4 had SGRQ ≥ 25, respectively. In unadjusted analyses, SGRQ ≥ 25 was associated with increased mortality hazards of 1.82 (95% CI 1.35-2.44; p<0.001), 1.91 (95% CI 1.47-2.49; p<0.001) and 2.04 (95% CI 1.41-2.95; p<0.001) in the NAO, GOLD 1-2 and GOLD 3-4 groups, respectively (Figure). These associations remained significant in adjusted analyses for NAO (p<0.001) and GOLD 1-2 (p=0.002), but not GOLD 3-4 (p=0.13). SGRQ ≥ 25 was an independent predictor of lung function decline in each of the NAO (p=0.02), GOLD 1-2 (p=0.01) and GOLD 3-4 (p=0.009) groups. Conclusion: Symptomatic smokers, even without airflow limitation, experience increased mortality and lung function decline.

Objectives: Tracheostomy utilization has dramatically increased recently. Large gaps exist between expected and actual outcomes resulting in significant decisional conflict and regret. We determined 1-year patient outcomes and healthcare utilization following tracheostomy to aid in decision-making and resource allocation. Design: Retrospective cohort study. Setting: All California hospital discharges from 2012 to 2013 with follow-up through 2014. Patients: Nonsurgical patients who received a tracheostomy for acute respiratory failure. Interventions: None. Measurements and Main Results: Our primary outcome was 30-day, 90-day, and 1-year mortality. We also determined hospitals readmissions rates and healthcare utilization in the first year following tracheostomy. We identified 8,343 tracheostomies during the study period. One-year mortality following tracheostomy was high, 46.5%. Older adults (≥ 65 yr) had significantly higher mortality compared with younger patients (< 65 yr) (54.7% vs 36.5%; p < 0.0001). Median survival for older adults was 175 days (95% CI, 150–202 d) compared with greater than 1 year for younger adults (adjusted hazard ratio, 1.25; 95% CI, 1.14–1.36). Within 1 year of tracheostomy, 60.3% of patients required hospital readmission. Older adults were more likely to be readmitted in the first year after tracheostomy compared with younger adults (66.1% vs 55.2%; adjusted hazard ratio, 1.19; 95% CI, 1.09–1.29). Total short-term acute care hospital costs (index and readmissions) in the first year after tracheostomy were high (mean, $215,369; sd , $160,874). Conclusions: Long-term outcomes following tracheostomy are extremely poor with high mortality, morbidity, and healthcare resource utilization especially among older patients. Some subsets of younger patients may have better outcomes compared with the general tracheostomy population. Short-term acute care costs were extremely high in the first year following tracheostomy. If extended to the entire U.S. population, total short-term acute care hospital costs approach $11 billion dollars per year for tracheostomy-related to acute respiratory failure. These findings may aid families and surrogates in the decision-making process.

In this issue of Temperature, Caldwell and Cheuvront summarize what they consider to be basic statistical considerations for occupational and environmental physiology [1]. In their Comprehensive Re...

Abstract. Current clinical chest CT reporting includes limited qualitative assessment of emphysema with rare mention of lung volumes and limited reporting of emphysema, based upon retrospective review of CT reports. Quantitative CT analysis performed in COPDGene and other research cohorts utilize semiautomated segmentation procedures and well-established research method (Thirona). We compared this reference QCT data with fully automated QCT analysis that can be obtained at the time of CT scan and sent to PACS along with standard chest CT images. 164 COPDGene® cohort study subjects enrolled at Brigham and Women’s Hospital had baseline and 5-year follow-up CT scans. Subjects included 17 nonsmoking controls, 92 smokers with normal spirometry, 15 preserved ratio impaired spirometry (PRISm) patients, 12 GOLD 1, 20 GOLD 2, and 8 GOLD 3–4. 97% (n  =  319) of clinical reports did not mention lung volumes, and 14% (n  =  46) made no mention of emphysema. Total lung volumes determined by the fully automated algorithm were consistently 47 milliliters (ml) less than the Thirona reference value for all subjects (95% confidence interval −62 to −32  ml). Percent emphysema values were equivalent to the Thirona reference values. Well-established research reference data can be used to evaluate and validate automated QCT software. Validation can be repeated as software is updated.

PURPOSE:The purpose of this study was to define the optimal scoring method for identifying benign intrapulmonary lymph nodes. MATERIALS AND METHODS:Subjects for this study were selected from the COPDGene study, a large multicenter longitudinal observational cohort study. A retrospective case-control analysis was performed using identified nodules on a subset of 377 patients who demonstrated 765 pulmonary nodules on their baseline computed tomography (CT) study. Nodule characteristics of 636 benign nodules (which resolved or showed <20% growth rate at 5 y follow-up) were compared with 51 nodules that occurred in the same lobe as a reported malignancy. Two radiologists scored each pulmonary nodule on the basis of intrapulmonary lymph node characteristics. A simple scoring strategy weighing all characteristics equally was compared with an optimized scoring strategy that weighed characteristics on the basis of their relative importance in identifying benign pulmonary nodules. RESULTS:A total of 479 of 636 benign pulmonary nodules had the majority of lymph node characteristics, whereas only 1 subpleural nodule with the majority of lymph node characteristics appeared to be malignant. Only 279 of 479 (58%) of benign pulmonary nodules with the majority of lymph node characteristics were intrafissural or subpleural. The optimized scoring strategy showed improved performance compared with the simple scoring strategy with average area under the curve of 0.80 versus 0.55. Optimized cutoff scores showed negative likelihood values for both readers of <0.2. A simulation showed a potential reduction in CT utilization of up to 36% for Fleischner criteria and up to 5% for LUNG-RADS. CONCLUSIONS:Nodules with the majority of lymph node characteristics, regardless of location, are likely benign, and weighing certain lymph node characteristics greater than others can improve overall performance. Given the potential to reduce CT utilization, lymph node characteristics should be considered when recommending appropriate follow-up.

Good presentation skills are important. Why? When you improve your presentation skills, you improve your ability to engage your audience and communicate whatever science you happen to do. In graduate school you learn how to do research, and you sometimes learn how to teach, but how often do you actually learn how to give an effective, engaging presentation? In this personal view I recount some of my early experiences as a presenter and some of the techniques I use to (hopefully) engage my audiences.

Objectives: Prior studies investigating hospital mechanical ventilation volume-outcome associations have had conflicting findings. Volume-outcome relationships within contemporary mechanical ventilation practices are unclear. We sought to determine associations between hospital mechanical ventilation volume and patient outcomes. Design: Retrospective cohort study. Setting: The California Patient Discharge Database 2016. Patients: Adult nonsurgical patients receiving mechanical ventilation. Interventions: The primary outcome was hospital death with secondary outcomes of tracheostomy and 30-day readmission. We used multivariable generalized estimating equations to determine the association between patient outcomes and hospital mechanical ventilation volume quartile. Measurements and Main Results: We identified 51,689 patients across 274 hospitals who required mechanical ventilation in California in 2016. 38.2% of patients died in the hospital with 4.4% receiving a tracheostomy. Among survivors, 29.5% required readmission within 30 days of discharge. Patients admitted to high versus low volume hospitals had higher odds of death (quartile 4 vs quartile 1 adjusted odds ratio, 1.40; 95% CI, 1.17–1.68) and tracheostomy (quartile 4 vs quartile 1 adjusted odds ratio, 1.58; 95% CI, 1.21–2.06). However, odds of 30-day readmission among survivors was lower at high versus low volume hospitals (quartile 4 vs quartile 1 adjusted odds ratio, 0.77; 95% CI, 0.67–0.89). Higher hospital mechanical ventilation volume was weakly correlated with higher hospital risk-adjusted mortality rates (ρ = 0.16; p = 0.008). These moderately strong observations were supported by multiple sensitivity analyses. Conclusions: Contrary to previous studies, we observed worse patient outcomes at higher mechanical ventilation volume hospitals. In the setting of increasing use of mechanical ventilation and changes in mechanical ventilation practices, multiple mechanisms of worse outcomes including resource strain are possible. Future studies investigating differences in processes of care between high and low volume hospitals are necessary.

Epithelial-fibroblast interactions are thought to be very important in the adult lung in response to injury, but the specifics of these interactions are not well defined. We developed coculture systems to define the interactions of adult human alveolar epithelial cells with lung fibroblasts. Alveolar type II cells cultured on floating collagen gels reduced the expression of type 1 collagen (COL1A1) and α-smooth muscle actin (ACTA2) in fibroblasts. They also reduced fibroblast expression of hepatocyte growth factor (HGF), fibroblast growth factor 7 (FGF7, KGF), and FGF10. When type II cells were cultured at an air-liquid interface to maintain high levels of surfactant protein expression, this inhibitory activity was lost. When type II cells were cultured on collagen-coated tissue culture wells to reduce surfactant protein expression further and increase the expression of some type I cell markers, the epithelial cells suppressed transforming growth factor-β (TGF-β)-stimulated ACTA2 and connective tissue growth factor (CTGF) expression in lung fibroblasts. Our results suggest that transitional alveolar type II cells and likely type I cells but not fully differentiated type II cells inhibit matrix and growth factor expression in fibroblasts. These cells express markers of both type II cells and type I cells. This is probably a normal homeostatic mechanism to inhibit the fibrotic response in the resolution phase of wound healing. Defining how transitional type II cells convert activated fibroblasts into a quiescent state and inhibit the effects of TGF-β may provide another approach to limiting the development of fibrosis after alveolar injury.