Bench to bedside: recent advances in lung cancer pathological research with implications for diagnostic clinical practice

After decades of relative stagnation lung cancer is emerging as a disease type where rapid progress is being made in diagnosis and therapy, as well as in our understanding of disease biology. Much of this progress is of immediate impact to diagnosticians, and more is likely to affect diagnostic practice in the near future. In this review we seek to briefly summarize several key areas of active research of immediate or probable imminent value to trainee and consultant pulmonary pathologists alike. We cover some major changes in tumour classification, grading, and patient stratification, as well as considering the state of the art in machine-assisted interpretation of lung cancer histology, and the use of genetically modified lung cancer models.     

直肠癌淋巴结转移的深度神经网络评判研究进展

正目前,临床上对于直肠癌常用的影像评估方法有MRI、螺旋CT、PET-CT、直肠腔内超声(ERUS)等。而MRI作为首选检查方式,对肿瘤位置、浸润深度、淋巴结转移、血管侵犯、环周切缘及周围器官侵犯等方面的评估均具有明显优势~([1-2])。通过MRI诊断淋巴结的方法通常是影像科医师逐层浏览每一幅图像,从中识别淋巴结的形状、界限及密度来判断,这种传统方式耗时较长且存在主观偏倚,导致     

Low potential for evolutionary rescue from climate change in a tropical fish

Climate change is increasing global temperatures and intensifying the frequency and severity of extreme heat waves. How organisms will cope with these changes depends on their inherent thermal tolerance, acclimation capacity, and ability for evolutionary adaptation. Yet, the potential for adaptation of upper thermal tolerance in vertebrates is largely unknown. We artificially selected offspring from wild-caught zebrafish (Danio rerio) to increase (Up-selected) or decrease (Down-selected) upper thermal tolerance over six generations. Selection to increase upper thermal tolerance was also performed on warm-acclimated fish to test whether plasticity in the form of inducible warm tolerance also evolved. Upper thermal tolerance responded to selection in the predicted directions. However, compared to the control lines, the response was stronger in the Down-selected than in the Up-selected lines in which evolution toward higher upper thermal tolerance was slow (0.04 ± 0.008 °C per generation). Furthermore, the scope for plasticity resulting from warm acclimation decreased in the Up-selected lines. These results suggest the existence of a hard limit in upper thermal tolerance. Considering the rate at which global temperatures are increasing, the observed rates of adaptation and the possible hard limit in upper thermal tolerance suggest a low potential for evolutionary rescue in tropical fish living at the edge of their thermal limits.

Globally, both mean and extreme environmental temperatures are increasing due to climate change with mean temperatures predicted to increase by 0.3–4.8 °C by the end of the century (1, 2). Aquatic ectotherms are particularly vulnerable to rising temperatures as their body temperature closely tracks the environmental temperature (3). These organisms can avoid thermal stress by migrating to cooler waters, acclimating, and/or adapting genetically (4–6). For species with a limited dispersal ability (e.g., species from shallow freshwater habitats; ref. 7), acclimation and evolutionary adaptation are the only possible strategies. Furthermore, for ectotherms living at the edge of their upper thermal limits, an increase in extreme temperatures may generate temperature peaks that exceed physiological limits and cause high mortality (5, 8–10). Although this is expected to cause strong selection toward higher upper thermal tolerance, it is largely unknown, particularly within vertebrates, whether and at what rate organisms may adapt by evolving their thermal limits (11–14). These are important issues because constrained or limited evolvability (15) of upper thermal tolerance could lead to population extinctions as climate change increases the severity of heat waves.Ectotherms can also increase their thermal limits through physiological and biochemical adjustments, in a process known as thermal acclimation when they are exposed to elevated temperatures for a period of time (16, 17). Thermal acclimation, sometimes called thermal compensation, is here used interchangeably with the term physiological plasticity as outlined by Seebacher et al. (18). In the wild, individuals may experience days or weeks of warmer temperatures prior to a thermal extreme. Through physiological plasticity, the severity of an ensuing thermal extreme may be reduced, thus increasing the chance for survival (19). Furthermore, in some cases, adaptation can be accelerated by plasticity (20–22). This requires that the physiological mechanisms responsible for acclimation are also (at least partly) involved in the acute response; that is, that there is a positive genetic correlation between physiological plasticity and (acute) upper thermal tolerance. It is therefore crucial to quantify the evolutionary potential of upper thermal tolerance of fish populations threatened by climate change (23, 24) and to understand the link between the evolutionary response of upper thermal tolerance and physiological plasticity.Previously detected evolution of upper thermal tolerance generally points toward a slow process (12, 13, 25–31). However, estimates of the evolutionary potential in upper thermal tolerance mostly come from studies on Drosophila (12, 25, 27, 32), and empirical evidence in aquatic ectotherms and specifically vertebrates is limited. The few studies that have been performed on fish show disparate responses to selection on heat tolerance even within the same species. Baer and Travis (33) detected no response to selection yet Doyle et al. (34) and Klerks et al. (28) detected selection responses with heritabilities of 0.2 in killifish (Heterandria formosa). Despite the typical asymmetry of thermal performance curves (3, 35), studies in vertebrates are limited to unidirectional estimates of evolutionary potential (28, 31, 33) or do not account for the direction of evolution when estimating heritability in upper thermal tolerance from breeding designs (36, 37). Furthermore, while several studies have found that populations with different thermal histories have evolved different levels of heat tolerance (29–31), we still lack a good understanding of how physiological plasticity within a generation, in response to a short heat exposure, interacts with genetic changes during evolution of thermal tolerance.To investigate possible asymmetry in the evolutionary potential of upper thermal tolerance in a vertebrate species, we artificially selected offspring of wild-caught zebrafish (Danio rerio) to increase and decrease upper thermal tolerance for six generations. Furthermore, to disentangle the contribution of acclimation from the genetic response to increase upper thermal tolerance, we selected two lines that were exposed to a period of warm acclimation prior to a thermal challenge. The size (>20,000 phenotyped fish) and duration (six generations) of this study are unique in a vertebrate species for a climate change-relevant selection experiment, and the results provide critical and robust information on how tropical fish may adapt to a changing climate.Being a freshwater and tropical species, zebrafish are likely to be especially vulnerable to climate change (7, 38). In the wild, zebrafish can already be found living only a few degrees below their thermal limits (17, 39) and live in shallow streams and pools (40) that have the potential to rapidly warm during heat waves. Zebrafish therefore represent a species living at the edge of its thermal limit in which rapid adaptation of thermal tolerance would be particularly beneficial for its survival. Wild-caught zebrafish originating from different sites in West Bengal, India (17, 40), were used to maximize the genetic diversity of the parental population. These wild-caught zebrafish (n = 2,265) served as parents of the starting F₀ generation (n = 1,800) on which we selected upper thermal tolerance for six generations. Upper thermal tolerance was measured as the critical thermal maximum (CT_max), a commonly used measure of an organism’s acute upper thermal tolerance (16, 41). CT_max is defined as the temperature at which an individual loses equilibrium (i.e., uncontrolled and disorganized swimming in zebrafish; ref. 42) during thermal ramping. Measuring CT_max is rapid, repeatable, and does not appear to harm zebrafish (42). CT_max is ecologically relevant because it is highly correlated with both tolerance to slow warming (43) and to the upper temperature range boundaries of wild aquatic ectotherms (9).Our selection experiment consisted of four treatment groups (Up-selected, Down-selected, Acclimated Up-selected, and Control) with two replicate lines in each treatment. We established these lines by selecting fish on their CT_max in the F₀ generation with each line consisting of 150 individuals (see Methods for further details of F₀ generation). The offspring of those fish formed the F₁ generation that consisted of 450 offspring in each line. At each generation, the Up, Down, and Control lines were all held at optimal temperature (28 °C) (39), whereas the Acclimated Up-selected lines were acclimated to a supraoptimal temperature (32 °C) for 2 wk prior to selection (17). From the F₁ to F₆ generations, we measured CT_max for all 450 fish in each line and selected the 33% with the highest CT_max in the Up-selected and in the Acclimated Up-selected lines, and the 33% with the lowest CT_max in the Down-selected lines. In the Control lines, 150 fish were randomly selected, measured, and retained. Thus, CT_max was measured on a total of 3,000 fish per generation and 150 individuals remained in each of the eight lines after selection, forming the parents for the next generation. The nonselected lines (Control) represented a control for the Up-selected and Down-selected lines, while the Up-selected lines represented a control for the Acclimated Up-selected lines, because these two treatments solely differed by the acclimation period to which the latter were exposed before selection. Thus, differences in CT_max between Up-selected and Acclimated Up-selected lines represent the contribution of physiological plasticity to upper thermal tolerance. If the difference between these two treatments increases during selection, it would suggest that plasticity increases during adaptation to higher CT_max (i.e., the slope the reaction norm describing the relationship between CT_max and acclimation temperature would become steeper).After six generations of selection, upper thermal tolerance had evolved in both the Up-selected and the Down-selected lines (Fig. 1). In the Up-selected lines, upper thermal tolerance increased by 0.22 ± 0.05 °C ($\bar{x}$ ± 1 SE) compared to the Control lines whereas the Down-selected lines displayed a mean upper thermal tolerance 0.74 ± 0.05 °C lower than the Control (Fig. 1B; estimates for replicated lines combined). The asymmetry in the response to selection was confirmed by the estimated realized heritability, which was more than twice as high in the Down-selected lines (h² = 0.24; 95% CI: 0.19–0.28) than in the Up-selected lines (h² = 0.10; 95% CI: 0.05–0.14; Fig. 2).Open in a separate windowFig. 1.Upper thermal tolerance (CT_max) of wild-caught zebrafish over six episodes of selection. Duplicated lines were selected for increased (Up-selected, orange lines and triangles) and decreased (Down-selected, blue lines and squares) upper thermal tolerance. In addition, we had two Control lines (green dashed lines and diamonds). The Up, Down, and Control lines were all acclimated to a temperature of 28 °C. In addition, two lines were selected for increased upper thermal tolerance after 2 wk of warm acclimation at 32 °C (Acclimated Up-selected, red lines and circles). At each generation, the mean and 95% CIs of each line are shown (n ∼ 450 individuals per line). (A) Absolute upper thermal tolerance values. (B) The response to selection in the Up and Down lines centered on the Control lines (dashed green line). Difference between Up-selected and Acclimated-Up lines are shown in Fig. 3. The rate of adaptation (°C per generation) is reported for each treatment using estimates obtained from linear mixed effects models using the Control-centered response in the Up-selected and Down-selected lines and the absolute response for the Acclimated-Up lines (SE = ±0.01 °C in all lines).Open in a separate windowFig. 2.Realized heritability (h²) of upper thermal tolerance (CT_max) in wild-caught zebrafish. The realized heritability was estimated for each treatment as the slope of the regression of the cumulative response to selection on the cumulative selection differential using mixed effect models passing through the origin with replicate as a random effect. Slopes are presented with their 95% CIs (shaded area) for the Down-selected lines (blue) and Up-selected lines (orange). Data points represent the mean of each replicate line (n ∼ 450) over six generations of selection. Average selection differentials are 0.57 (Down) and 0.39 (Up), respectively, see SI Appendix, Table S1 for more information.At the start of the experiment (F₀), warm acclimation (32 °C) increased thermal tolerance by 1.31 ± 0.05 °C (difference in CT_max between the Up-selected and Acclimated Up-selected lines in Figs. 1A and ​and3),3), which translates to a 0.3 °C change in CT_max per 1 °C of warming. In the last generation, the effect of acclimation had decreased by 25%, with the Acclimated-Up lines having an average CT_max 0.98 ± 0.04 °C higher than the Up lines (Fig. 3). This suggests that, despite a slight increase in CT_max in the Acclimated Up-selected lines during selection, the contribution of plasticity decreased over the course of the experiment.Open in a separate windowFig. 3.Contribution of acclimation to the upper thermal tolerance in the Acclimated-Up selected lines at each generation of selection. The contribution of acclimation was estimated as the difference between the Up and Acclimated-Up selected lines. Points and error bars represent the estimates (±SE) from a linear mixed effects model with CT_max as the response variable; Treatment (factor with two levels: Up and Acclimated Up), Generation (factor with seven levels), and their interaction as the predictor variables; and replicate line as a random factor.During the experiment, the phenotypic variation of CT_max that was left-skewed at F₀ increased in the Down-selected lines and decreased in the Up-selected lines (Fig. 4). At the F₆ generation, phenotypic variance was four times lower in the Up-selected lines (0.09 ± 0.01 and 0.12 ± 0.02 °C²; variance presented for each replicate line separately and SE obtained by nonparametric bootstrapping) than in the Down-selected lines (0.41 ± 0.03 and 0.50 ± 0.04 °C²), which had doubled since the start of the experiment (F₀: 0.20 ± 0.01 °C², see SI Appendix, Fig. S1). In the Acclimated Up-selected lines, the phenotypic variance that was already much lower than the Control at the F₀ also decreased and reached 0.06 ± 0.01 °C² and 0.07 ± 0.01 °C² for the two replicates at the last generation (SI Appendix, Fig. S1).Open in a separate windowFig. 4.Distribution of upper thermal tolerance (CT_max) in selected lines. (A) Distribution for each line at each generation (F₀ to F₆). In the F₀ generation, histograms show the preselection distribution in gray for the nonacclimated fish, in dark green for the Control lines, and in red for the Acclimated-Up fish. In all subsequent generations the Down-selected lines are in blue, the Up-selected lines in yellow, the Control lines in dark green, and Acclimated-up lines in red. All treatments use two shades, one for each replicate line. Dashed lines represent the mean CT_max for each line (n ∼ 450 individuals). (B) Distribution of upper thermal tolerance at the start (F₀, in gray) and the end (F₆, in blue and yellow) of the experiment for the Up-selected and Down-selected lines. The dashed gray line represents the mean of the Up-selected and Down-selected lines in the F₀ generation preselection (n ∼ 900 individuals). Dashed blue and yellow lines represent the mean CT_max for Up and Down-selected lines for the F₆ generation (n ∼ 450 individuals).Together with the asymmetrical response to selection and the lower response of the Acclimated Up-selected lines, these changes in phenotypic variance suggest the existence of a hard-upper limit for thermal tolerance (e.g., major protein denaturation (44), similar to the “concrete ceiling” for physiological responses to warming (14)). Such a hard-upper limit is expected to generate a nonlinear mapping of the genetic and environmental effects on the phenotypic expression of CT_max. This nonlinearity will affect the phenotypic variance of CT_max when mean CT_max approaches its upper limit (SI Appendix, Fig. S2A). For example, with directional selection toward higher CT_max, genetic changes in upper thermal tolerance will translate into progressively smaller phenotypic changes. Similarly, warm acclimation that shifts CT_max upwards will also decrease phenotypic variation in CT_max (see differences in phenotypic variance between control and Acclimated lines at the F₀). This hard ceiling can also explain why an evolutionary increase in CT_max reduces the magnitude of physiological plasticity in CT_max achieved after a period of acclimation (Fig. 3 and see SI Appendix, Fig. S2B). If the sum of the genetic and plastic contributions to CT_max cannot exceed a ceiling value, this should generate a zero-sum gain between the genetic and plastic determinants of thermal tolerance. An increase in the genetic contribution to CT_max via selection should thus decrease the contribution of plasticity. Selection for a higher CT_max should therefore negatively affect the slope of the reaction norm of thermal acclimation because acclimation will increase CT_max more strongly at low than high acclimation temperature (SI Appendix, Fig. S2B).To test this hypothesis, we measured CT_max in all selected lines at the final generation (F₆) after acclimation to 24, 28, and 32 °C. At all three acclimation temperatures, the Acclimated-Up lines did not differ from the Up-selected lines (average difference 0.14 ± 0.08 °C; 0.12 ± 0.09 °C; 0.14 ± 0.09 °C; at 24, 28, and 32 °C respectively; Fig. 5). This suggests that warm acclimation prior to selection did not affect the response to selection. However, considering the within-treatment differences in CT_max between fish acclimated to 28 and 32 °C, we show that the gain in CT_max due to acclimation decreases in both the Up and Acclimated-Up treatments compared to the Control and Down treatments (SI Appendix, Fig. S3). This confirms a loss of thermal plasticity in both Up-selected treatments (Up and Acclimated-Up) at higher acclimation temperatures. Notably, the loss of thermal plasticity is not evident in fish acclimated to 24 and 28 °C, possibly because at these temperatures CT_max remains further away from its hard upper limit.Open in a separate windowFig. 5.Upper thermal tolerance (CT_max) of the selected lines measured at the last generation (F₆) after acclimation at 24, 28, and 32 °C. The response is calculated as the mean difference in upper thermal tolerance (CT_max) relative to the Control lines. Large points and whiskers represent mean ±1 SE for each treatment (n = 120 individuals): Up-selected (orange triangles), Down-selected (blue squares), Acclimated Up-selected (red circles), and Control (green diamonds). Smaller translucent points represent means of each replicate line (n = 60 individuals). See SI Appendix, Fig. S3 for absolute CT_max values and model estimates.Acclimated Up-selected lines are perhaps the most ecologically relevant in our selection experiment. In the wild, natural selection on upper thermal tolerance may not result from increasing mean temperatures but through rapid heating events such as heat waves (45). During heat waves, temperature may rise for days before reaching critical temperatures. This gives individuals the possibility to acclimate and increase their upper thermal tolerance prior to peak temperatures. Our results show that while warm acclimation allowed individuals to increase their upper thermal tolerance, it did not increase the magnitude or the rate of adaptation of upper thermal tolerance.For the past two decades it has been recognized that rapid evolution, at ecological timescales, occurs and may represent an essential mechanism for the persistence of populations in rapidly changing environments (24, 46, 47). Yet, in the absence of an explicit reference, rates of evolution are often difficult to categorize as slow or rapid (48). For traits related to thermal tolerance or thermal performance, this issue is complicated by the fact that the scale on which traits are measured (temperature in °C) cannot meaningfully be transformed to a proportional scale. This prevents us from comparing rates of evolution between traits related to temperature with other traits measured on different scales (49, 50). However, for thermal tolerance, the rate of increase in ambient temperature predicted over the next century represents a particularly meaningful standard against which the rate of evolution observed in our study can be compared.In India and surrounding countries where zebrafish are native, heat waves are predicted to increase in frequency, intensity, and duration, and maximum air temperatures in some regions are predicted to exceed 44 °C in all future climate scenarios (51). Air temperature is a good predictor of water temperature in shallow ponds and streams where wild zebrafish are found (17, 40, 52, 53). Thus, strong directional selection on the thermal limits of zebrafish is very likely to occur in the wild. At first sight, changes in the upper thermal tolerance observed in our study (0.04 °C per generation) as well as the heritability estimates (Down-selected: h² = 0.24, Up-selected: h² = 0.10) similar to those obtained in fruit flies (Drosophila melanogaster) selected for acute upper thermal tolerance (Down-selected: h² = 0.19, Up-selected: h² = 0.12; ref. 12), suggest that zebrafish may just be able to keep pace with climate change and acutely tolerate temperatures of 44 °C predicted by the end of the century. However, several cautions make such an optimistic prediction unlikely.First, such an extrapolation assumes a generation time of 1 y, which is likely for zebrafish but unrealistic for many other fish species. Second, such a rate of evolution is associated with a thermal culling of two-thirds of the population at each generation, a strength of selection that may be impossible to sustain in natural populations exposed to other selection pressures such as predation or harvesting. Third, the heritability and rate of adaptation toward higher upper thermal tolerance observed here may be considered as upper estimates because of the potentially high genetic variance harbored by our parental population where samples from several sites were mixed. While mixing of zebrafish populations often occurs in the wild during monsoon flooding (54, 55), there are likely to be some isolated populations that may have a lower genetic diversity and adaptation potential than our starting population. Finally, and most importantly, the reduced phenotypic variance and decreased acclimation capacity with increasing CT_max observed in our study suggest the existence of a hard-upper limit to thermal tolerance that will lead to an evolutionary plateau similar to those reached in Drosophila selected for increased heat resistance over many generations (12, 56). Overall, the rate of evolution observed in our study is likely higher than what will occur in the wild and, based on this, it seems unlikely that zebrafish, or potentially other tropical fish species, will be able to acutely tolerate temperatures predicted by the end of the century. It is possible that other fish species, especially those living in cooler waters and with wider thermal safety margins, will display higher rates of adaptation than the ones we observed here, and more studies of this kind in a range of species are needed to determine whether slow adaptation of upper thermal tolerance is a general phenomenon.Transgenerational plasticity (e.g., epigenetics) has been suggested to modulate physiological thermal tolerance (57). However, the progressive changes in CT_max observed across generations in our study indicate that these changes were primarily due to genetic changes because effects of transgenerational plasticity are not expected to accumulate across generations. Therefore, the effects of transgenerational plasticity in the adaptation of upper thermal tolerance may be insufficient to mitigate impacts of climate change on zebrafish, yet the potential contribution of transgenerational plasticity is still an open question.By phenotyping more than 20,000 fish over six generations of selection, we show that evolution of upper thermal tolerance is possible in a vertebrate over short evolutionary time. However, the evolutionary potential for increased upper thermal tolerance is low due to the slow rate of adaptation compared to climate warming, as well as the diminishing effect of acclimation as adaptation progresses. Our results thus suggest that fish populations, especially warm water species living close to their thermal limits, may struggle to adapt with the rate at which water temperatures are increasing.     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.     

Respiratory support in children

Respiratory failure is defined by the inability of the respiratory system to adequately deliver oxygen or remove carbon dioxide from the pulmonary circulation resulting in hypoxemia, hypercapnia or both. A wide variety of disease processes can lead to respiratory failure in children. Multiple interventions can support the paediatric patient with respiratory failure, from simple oxygen delivery devices to high frequency oscillatory ventilation and extracorporeal membrane oxygenation. This article will review available devices to improve oxygenation and ventilation, their advantages and disadvantages, and help guide physicians in the management of children with respiratory failure.     

浅谈哈萨克斯坦荒漠草地现状与改良利用

中国新疆与哈萨克斯坦共和国生态环境特点极为相似,荒漠、半荒漠草地类型占草原面积的首位。由草原研究所派出的科技交流团,先后考察了哈国荒漠草地现状与改良利用情况,就优良旱生牧草新品种选育和荒漠、半荒漠区无灌溉条件下人工草地建植与管理技术模式达成技术合作事宜。     

Comparison of diagnostic usefulness of predictive models in preliminary differentiation of adnexal masses

The purpose of this study was to compare prognostic models evaluating the probability of an ovarian cancer occurrence based on a number of clinical and ultrasonographic data in women with adnexal masses. A total of 686 women with adnexal masses underwent the examinations between 1994 and 2002. The recorded parameters included: age, menopausal status, body mass index, the grayscale and Doppler ultrasonographic examination, and selected markers concentration levels. In order to find the best combination of features, which significantly influences the probability of malignancy, stepwise logistic regression analysis, as well as artificial neural network, was used. The diagnostic efficiency of received models was estimated and compared using receiver-operating characteristics (ROC) curve. The results indicate that 431 and 255 patients had a benign and malignant ovarian tumor, respectively. Application of stepwise logistic regression analysis revealed statistically significant importance of eight features. The sensitivity and specificity for the received model were 65.71% and 77.59%, respectively. Three-layer perceptron network shows 13 features as significant predictors of malignancy. The network gave a sensitivity of 85.7% and specificity of 93.1%. Comparison of area under ROC curve for received models was 0.9679 vs 0.9716. Prognostic values of the analyzed neural model are not optimal but seem to surpass logistic regression model in terms of the predictive possibilities.     

老年危重病人人工气道气囊压力监测及护理

[目的 ]探讨人工气道气囊压力监测在老年危重病人气管导管护理中的应用价值。 [方法 ]将 60例建立人工气道的老年危重病人随机分成两组 ,实验组应用PORTEX专用气囊压力监测表注气测压 ,对照组采用传统的手指捏感法注气 ;用呼吸机检查漏气情况 ,并比较两组病人的气囊注气容积、气囊压力及并发症发生率。 [结果 ]实验组气囊压力为 2 .45 2kPa±0 .490kPa时 ,气囊容积为 10 .0mL± 4.7mL ,呼吸机检查不漏气 ;对照组气囊压力为 3 .92 3kPa± 0 .73 6kPa时 ,气囊容积为 15 .0mL± 5 .1mL ,呼吸机检查不漏气。两组病人气管黏膜损伤、气囊破裂发生率比较有统计学意义 (P <0 .0 5 ) ;两组病人误吸、食管气管瘘发生率比较无统计学意义 (P >0 .0 5 )。 [结论 ]建立人工气道的老年危重病人气囊压力维持在1.961kPa～ 2 .942kPa ,能有效避免误吸的发生和气管黏膜的损伤。