The impact of a lung-protective ventilation mode using transpulmonary driving pressure titrated positive end-expiratory pressure on the prognosis of patients with acute respiratory distress syndrome.
This study aimed to assess the impact of a lung-protective ventilation strategy utilizing transpulmonary driving pressure titrated positive end-expiratory pressure (PEEP) on the prognosis [mechanical ventilation duration, hospital stay, 28-day mortality rate and incidence of ventilator-associated pneumonia (VAP), survival outcome] of patients with Acute Respiratory Distress Syndrome (ARDS).
A total of 105 ARDS patients were randomly assigned to either the control group (n = 51) or the study group (n = 53). The control group received PEEP titration based on tidal volume [A tidal volume of 6 mL/kg, flow rate of 30-60 L/min, frequency of 16-20 breaths/min, constant flow rate, inspiratory-to-expiratory ratio of 1:1 to 1:1.5, and a plateau pressure ≤ 30-35 cmH2O. PEEP was adjusted to maintain oxygen saturation (SaO2) at or above 90%, taking into account blood pressure], while the study group received PEEP titration based on transpulmonary driving pressure (Esophageal pressure was measured as a surrogate for pleural pressure using an esophageal pressure measurement catheter connected to the ventilator. Tidal volume and PEEP were adjusted based on the observed end-inspiratory and end-expiratory transpulmonary pressures, aiming to maintain a transpulmonary driving pressure below 15 cmH2O during mechanical ventilation. Adjustments were made 2-4 times per day). Statistical analysis and comparison were conducted on lung function indicators [oxygenation index (OI), arterial oxygen tension (PaO2), arterial carbon dioxide tension (PaCO2)] as well as other measures such as heart rate, mean arterial pressure, and central venous pressure in two groups of patients after 48 h of mechanical ventilation. The 28-day mortality rate, duration of mechanical ventilation, length of hospital stay, and ventilator-associated pneumonia (VAP) incidence were compared between the two groups. A 60-day follow-up was performed to record the survival status of the patients.
In the control group, the mean age was (55.55 ± 10.51) years, with 33 females and 18 males. The pre-ICU hospital stay was (32.56 ± 9.89) hours. The mean Acute Physiology and Chronic Health Evaluation (APACHE) II score was (19.08 ± 4.67), and the mean Murray Acute Lung Injury score was (4.31 ± 0.94). In the study group, the mean age was (57.33 ± 12.21) years, with 29 females and 25 males. The pre-ICU hospital stay was (33.42 ± 10.75) hours. The mean APACHE II score was (20.23 ± 5.00), and the mean Murray Acute Lung Injury score was (4.45 ± 0.88). They presented a homogeneous profile (all P > 0.05). Following intervention, significant improvements were observed in PaO2 and OI compared to pre-intervention values. The study group exhibited significantly higher PaO2 and OI compared to the control group, with statistically significant differences (all P < 0.05). After intervention, the study group exhibited a significant increase in PaCO2 (43.69 ± 6.71 mmHg) compared to pre-intervention levels (34.19 ± 5.39 mmHg). The study group's PaCO2 was higher than the control group (42.15 ± 7.25 mmHg), but the difference was not statistically significant (P > 0.05). There were no significant differences in hemodynamic indicators between the two groups post-intervention (all P > 0.05). The study group demonstrated significantly shorter mechanical ventilation duration and hospital stay, while 28-day mortality rate and incidence of ventilator-associated pneumonia (VAP) showed no significant differences. Kaplan-Meier survival analysis revealed a significantly better survival outcome in the study group at the 60-day follow-up (HR = 0.565, 95% CI: 0.320-0.999).
Lung-protective mechanical ventilation using transpulmonary driving pressure titrated PEEP effectively improves lung function, reduces mechanical ventilation duration and hospital stay, and enhances survival outcomes in patients with ARDS. However, further study is needed to facilitate the wider adoption of this approach.
Sun J
，Gao J
，Huang GD
，Zhu XG
，Yang YP
，Zhong WX
，Geng L
，Zhou MJ
，Xu Q
，Feng QM
，Zhao G
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Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.
Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided.
(1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS?
Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient's survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the "null model" assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model's predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether laboratory data, which were not included in the METSSS model, would have been independently associated with survival after controlling for the METSSS model's predictions by using the multivariable logistic and Cox proportional hazards regression analyses.
Between the two models, only SORG-MLA achieved adequate discrimination (an AUROC of > 0.7) in the entire cohort (of patients treated operatively or with radiation alone) and in the subgroup of patients treated with palliative radiotherapy alone. SORG-MLA outperformed METSSS by a wide margin on discrimination, calibration, and Brier score analyses in not only the entire cohort but also the subgroup of patients whose local treatment consisted of radiotherapy alone. In both the entire cohort and the subgroup, DCA demonstrated that SORG-MLA provided more net benefit compared with the two default strategies (of treating all or no patients) and compared with METSSS when risk thresholds ranged from 0.2 to 0.9 at both 90 days and 1 year, indicating that using SORG-MLA as a decision-making aid was beneficial when a patient's individualized risk threshold for opting for treatment was 0.2 to 0.9. Higher albumin, lower alkaline phosphatase, lower calcium, higher hemoglobin, lower international normalized ratio, higher lymphocytes, lower neutrophils, lower neutrophil-to-lymphocyte ratio, lower platelet-to-lymphocyte ratio, higher sodium, and lower white blood cells were independently associated with better 1-year and overall survival after adjusting for the predictions made by METSSS.
Based on these discoveries, clinicians might choose to consult SORG-MLA instead of METSSS for survival estimation in patients with long-bone metastases presenting for evaluation of local treatment. Basing a treatment decision on the predictions of SORG-MLA could be beneficial when a patient's individualized risk threshold for opting to undergo a particular treatment strategy ranged from 0.2 to 0.9. Future studies might investigate relevant laboratory items when constructing or refining a survival estimation model because these data demonstrated prognostic value independent of the predictions of the METSSS model, and future studies might also seek to keep these models up to date using data from diverse, contemporary patients undergoing both modern operative and nonoperative treatments.
Level III, diagnostic study.
Lee CC
，Chen CW
，Yen HK
，Lin YP
，Lai CY
，Wang JL
，Groot OQ
，Janssen SJ
，Schwab JH
，Hsu FM
，Lin WH
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ResearchGate
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钛学术
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