Clinical efficacy of HI-NPPV in the treatment of AECOPD combined with severe type II respiratory failure. / HI-NPPV治疗AECOPD合并严重II型呼吸衰竭的临床疗效.

OBJECTIVES: Patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) combined with severe type II respiratory failure have a high probability of ventilation failure using conventional non-invasive positive pressure ventilation (NPPV). This study aims to investigate the clinical efficacy of high intensity NPPV (HI-NPPV) for the treatment of AECOPD combined with severe type II respiratory failure. METHODS: The data of patients with AECOPD combined with severe type II respiratory failure (blood gas analysis pH≤7.25) treated with NPPV in the Second Affiliated Hospital of Chongqing Medical University from July 2013 to July 2023 were collected to conduct a retrospective case-control study. The patients were divided into 2 groups according to the inspired positive airway pressure (IPAP) used during the NPPV treatment: a NPPV group (IPAP<20 cmH2O, 1 cmH2O=0.098 kPa) and a HI-NPPV group (20 cmH2O≤IPAP< 30 cmH2O). Ninety-nine and 95 patients were included in the NPPV group and the HI-NPPV group, respectively. A total of 86 pairs of data were matched using propensity score matching (PSM) for data matching. The primary outcome indexes (mortality and tracheal intubation rate) and secondary outcome indexes [blood gas analysis pH, arterial partial pressure of oxygen (PaO2) and arterial partial pressure of carbon dioxide (PaCO2), adverse reaction rate, and length of hospitalization] were compared between the 2 groups. RESULTS: The tracheal intubation rates of the NPPV group and the HI-NPPV group were 6.98% and 1.16%, respectively, and the difference between the 2 groups was statistically significant (χ2=4.32, P<0.05); the mortality of the NPPV group and the HI-NPPV group was 23.26% and 9.30%, respectively, and the difference between the 2 groups was statistically significant (χ2=11.64, P<0.01). The PaO2 at 24 h and 48 h after treatment of the HI-NPPV group was higher than that of the NPPV group, and the PaCO2 of the HI-NPPV group was lower than that of the NPPV group, and the differences were statistically significant (all P<0.05). The differences of pH at 24 h and 48 h after treatment between the 2 groups were not statistically significant (both P>0.05). The differences between the 2 groups in adverse reaction rate and hospitalization length were not statistically significant (both P>0.05). CONCLUSIONS: HI-NPPV can reduce mortality and tracheal intubation rates by rapidly improving the ventilation of patients with AECOPD combined with severe type II respiratory failure. This study provides a new idea for the treatment of patients with AECOPD combined with severe type II respiratory failure.

A novel method to establish the rabbit model of knee osteoarthritis: intra-articular injection of SDF-1 induces OA.

BACKGROUND: Animal model of Knee Osteoarthritis (OA) is the primary testing methodology for studies on pathogenic mechanisms and therapies of human OA disease. Recent major modeling methods are divided into artificially induced and spontaneous. However, these methods have some disadvantages of slow progression, high cost and no correlation with the pathogenesis of OA. METHODS: Our studies attempted to find a rapid, easy, and consistent with the natural pathological process of OA modeling method by intra-articular injection of stromal cell-derived factor 1 (SDF-1) in the rabbit knee. After induction we collected cartilage specimens from the medial femoral condyle to undergo macroscopic, histological, immunohistochemical, and biochemical evaluations. Meanwhile, compared with Hulth surgical method to evaluate its efficacy. RESULTS: Macroscopic observation and modified Mankin score of histological staining exhibited typical features of middle stage OA cartilage in SDF-1 injected groups. Immunohistochemically, the positive expression of interleukin-1 (IL-1) and tumor necrosis factor α(TNF-α) was earlier and higher in high dose SDF-1 group than the surgical group. The matrix metalloproteinases (MMPs) in synovial fluid and chondrocytes significantly increased, but type II collagen (COLII) and aggrecan (ACAN) protein expressions decreased in SDF-1 injected group following the extension of time and increase of SDF-1 concentration. CONCLUSIONS: Our data indicated intra-articular injection of SDF-1 (40µg/kg, three times for 12 weeks) can induce rabbit knee OA model successfully more rapidly and easily than traditional surgical modeling. The study provided a further option for the establishment of knee OA animal model.

Maximum information per time unit designs for continuous online item calibration.

Previous designs for online calibration have only considered examinees' responses to items. However, the use of response time, a useful metric that can easily be collected by a computer, has not yet been embedded in calibration designs. In this article we utilize response time to optimize the assignment of new items online, and accordingly propose two new adaptive designs. These are the D-optimal per expectation time unit design (D-ET) and the D-optimal per time unit design (D-T). The former method uses the conditional maximum likelihood estimation (CMLE) method to estimate the expected response times, while the latter employs the nonparametric k-nearest-neighbour method to predict the response times. Simulations were conducted to compare the two new designs with the D-optimal online calibration design (D design) in the context of continuous online calibration. In addition, a preliminary study was carried out to evaluate the performance of CMLE prior to its application in D-ET. The results showed that, compared to the D design, the D-ET and D-T designs saved response time and accrued larger calibration information per time unit, without sacrificing item calibration precision.

Optimal Online Calibration Designs for Item Replenishment in Adaptive Testing.

The maintenance of item bank is essential for continuously implementing adaptive tests. Calibration of new items online provides an opportunity to efficiently replenish items for the operational item bank. In this study, a new optimal design for online calibration (referred to as D-c) is proposed by incorporating the idea of original D-optimal design into the reformed D-optimal design proposed by van der Linden and Ren (Psychometrika 80:263-288, 2015) (denoted as D-VR design). To deal with the dependence of design criteria on the unknown item parameters of new items, Bayesian versions of the locally optimal designs (e.g., D-c and D-VR) are put forward by adding prior information to the new items. In the simulation implementation of the locally optimal designs, five calibration sample sizes were used to obtain different levels of estimation precision for the initial item parameters, and two approaches were used to obtain the prior distributions in Bayesian optimal designs. Results showed that the D-c design performed well and retired smaller number of new items than the D-VR design at almost all levels of examinee sample size; the Bayesian version of D-c using the prior obtained from the operational items worked better than that using the default priors in BILOG-MG and PARSCALE; and Bayesian optimal designs generally outperformed locally optimal designs when the initial item parameters of the new items were poorly estimated.

New Efficient and Practicable Adaptive Designs for Calibrating Items Online.

When calibrating new items online, it is practicable to first compare all new items according to some criterion and then assign the most suitable one to the current examinee who reaches a seeding location. The modified D-optimal design proposed by van der Linden and Ren (denoted as D-VR design) works within this practicable framework with the aim of directly optimizing the estimation of item parameters. However, the optimal design point for a given new item should be obtained by comparing all examinees in a static examinee pool. Thus, D-VR design still has room for improvement in calibration efficiency from the view of traditional optimal design. To this end, this article incorporates the idea of traditional optimal design into D-VR design and proposes a new online calibration design criterion, namely, excellence degree (ED) criterion. Four different schemes are developed to measure the information provided by the current examinee when implementing this new criterion, and four new ED designs equipped with them are put forward accordingly. Simulation studies were conducted under a variety of conditions to compare the D-VR design and the four proposed ED designs in terms of calibration efficiency. Results showed that the four ED designs outperformed D-VR design in almost all simulation conditions.

A New Online Calibration Method Based on Lord's Bias-Correction.

Online calibration technique has been widely employed to calibrate new items due to its advantages. Method A is the simplest online calibration method and has attracted many attentions from researchers recently. However, a key assumption of Method A is that it treats person-parameter estimates θ/ (obtained by maximum likelihood estimation [MLE]) as their true values θs , thus the deviation of the estimated θ/ from their true values might yield inaccurate item calibration when the deviation is nonignorable. To improve the performance of Method A, a new method, MLE-LBCI-Method A, is proposed. This new method combines a modified Lord's bias-correction method (named as maximum likelihood estimation-Lord's bias-correction with iteration [MLE-LBCI]) with the original Method A in an effort to correct the deviation of θ/ which may adversely affect the item calibration precision. Two simulation studies were carried out to explore the performance of both MLE-LBCI and MLE-LBCI-Method A under several scenarios. Simulation results showed that MLE-LBCI could make a significant improvement over the ML ability estimates, and MLE-LBCI-Method A did outperform Method A in almost all experimental conditions.