Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

BACKGROUND

Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications.

METHODS

We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC).

FINDINGS

In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683-0·717]).

INTERPRETATION

In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required.

FUNDING

British Journal of Surgery Society.

Rational design, synthesis and testing of novel tricyclic topoisomerase inhibitors for the treatment of bacterial infections part 1

The alarming reduction in drug effectiveness against bacterial infections has created an urgent need for the development of new antibacterial agents that circumvent bacterial resistance mechanisms. We report here a series of DNA gyrase and topoisomerase IV inhibitors that demonstrate potent activity against a range of Gram-positive and selected Gram-negative organisms, including clinically-relevant and drug-resistant strains. In part 1, we present a detailed structure activity relationship (SAR) analysis that led to the discovery of our previously disclosed compound, REDX05931, which has a minimum inhibitory concentration (MIC) of 0.06 μg mL^-1 against fluoroquinolone-resistant Staphylococcus aureus. Although in vitro hERG and CYP inhibition precluded further development, it validates a rational design approach to address this urgent unmet medical need and provides a scaffold for further optimisation, which is presented in part 2.

Rational design, synthesis and testing of novel tricyclic topoisomerase inhibitors for the treatment of bacterial infections part 2

Building on our previously-reported novel tricyclic topoisomerase inhibitors (NTTIs), we disclose the discovery of REDX07965, which has an MIC90 of 0.5 μg mL-1 against Staphylococcus aureus, favourable in vitro pharmacokinetic properties, selectivity versus human topoisomerase II and an acceptable toxicity profile. The results herein validate a rational design approach to address the urgent unmet medical need for novel antibiotics.

Blocking FcRn in humans reduces circulating IgG levels and inhibits IgG immune complex-mediated immune responses

The neonatal crystallizable fragment receptor (FcRn) functions as an intracellular protection receptor for immunoglobulin G (IgG). Recently, several clinical studies have reported the lowering of circulating monomeric IgG levels through FcRn blockade for the potential treatment of autoimmune diseases. Many autoimmune diseases, however, are derived from the effects of IgG immune complexes (ICs). We generated, characterized, and assessed the effects of SYNT001, a FcRn-blocking monoclonal antibody, in mice, nonhuman primates (NHPs), and humans. SYNT001 decreased all IgG subtypes and IgG ICs in the circulation of humans, as we show in a first-in-human phase 1, single ascending dose study. In addition, IgG IC induction of inflammatory pathways was dependent on FcRn and inhibited by SYNT001. These studies expand the role of FcRn in humans by showing that it controls not only IgG protection from catabolism but also inflammatory pathways associated with IgG ICs involved in a variety of autoimmune diseases.

Optimizing beam models for dosimetric accuracy over a wide range of treatments

This work presents a systematic approach for testing a dose calculation algorithm over a variety of conditions designed to span the possible range of clinical treatment plans. Using this method, a TrueBeam STx machine with high definition multi-leaf collimators (MLCs) was commissioned in the RayStation treatment planning system (TPS). The initial model parameters values were determined by comparing TPS calculations with standard measured depth dose and profile curves. The MLC leaf offset calibration was determined by comparing measured and calculated field edges utilizing a wide range of MLC retracted and over-travel positions. The radial fluence was adjusted using profiles through both the center and corners of the largest field size, and through measurements of small fields that were located at highly off-axis positions. The flattening filter source was adjusted to improve the TPS agreement for the output of MLC-defined fields with much larger jaw openings. The MLC leaf transmission and leaf end parameters were adjusted to optimize the TPS agreement for highly modulated intensity-modulated radiotherapy (IMRT) plans. The final model was validated for simple open fields, multiple field configurations, the TG 119 C-shape target test, and a battery of clinical IMRT and volumetric-modulated arc therapy (VMAT) plans. The commissioning process detected potential dosimetric errors of over 10% and resulted in a final model that provided in general 3% dosimetric accuracy. This study demonstrates the importance of using a variety of conditions to adjust a beam model and provides an effective framework for achieving high dosimetric accuracy.

Deep nets vs expert designed features in medical physics: An IMRT QA case study

PURPOSE

The purpose of this study was to compare the performance of Deep Neural Networks against a technique designed by domain experts in the prediction of gamma passing rates for Intensity Modulated Radiation Therapy Quality Assurance (IMRT QA).

METHOD

A total of 498 IMRT plans across all treatment sites were planned in Eclipse version 11 and delivered using a dynamic sliding window technique on Clinac iX or TrueBeam Linacs. Measurements were performed using a commercial 2D diode array, and passing rates for 3%/3 mm local dose/distance-to-agreement (DTA) were recorded. Separately, fluence maps calculated for each plan were used as inputs to a convolution neural network (CNN). The CNNs were trained to predict IMRT QA gamma passing rates using TensorFlow and Keras. A set of model architectures, inspired by the convolutional blocks of the VGG-16 ImageNet model, were constructed and implemented. Synthetic data, created by rotating and translating the fluence maps during training, was created to boost the performance of the CNNs. Dropout, batch normalization, and data augmentation were utilized to help train the model. The performance of the CNNs was compared to a generalized Poisson regression model, previously developed for this application, which used 78 expert designed features.

RESULTS

Deep Neural Networks without domain knowledge achieved comparable performance to a baseline system designed by domain experts in the prediction of 3%/3 mm Local gamma passing rates. An ensemble of neural nets resulted in a mean absolute error (MAE) of 0.70 ± 0.05 and the domain expert model resulted in a 0.74 ± 0.06.

CONCLUSIONS

Convolutional neural networks (CNNs) with transfer learning can predict IMRT QA passing rates by automatically designing features from the fluence maps without human expert supervision. Predictions from CNNs are comparable to a system carefully designed by physicist experts.

A manually annotated Actinidia chinensis var. chinensis (kiwifruit) genome highlights the challenges associated with draft genomes and gene prediction in plants

Background

Most published genome sequences are drafts, and most are dominated by computational gene prediction. Draft genomes typically incorporate considerable sequence data that are not assigned to chromosomes, and predicted genes without quality confidence measures. The current Actinidia chinensis (kiwifruit) ‘Hongyang’ draft genome has 164 Mb of sequences unassigned to pseudo-chromosomes, and omissions have been identified in the gene models.

Results

A second genome of an A. chinensis (genotype Red5) was fully sequenced. This new sequence resulted in a 554.0 Mb assembly with all but 6 Mb assigned to pseudo-chromosomes. Pseudo-chromosomal comparisons showed a considerable number of translocation events have occurred following a whole genome duplication (WGD) event some consistent with centromeric Robertsonian-like translocations. RNA sequencing data from 12 tissues and ab initio analysis informed a genome-wide manual annotation, using the WebApollo tool. In total, 33,044 gene loci represented by 33,123 isoforms were identified, named and tagged for quality of evidential support. Of these 3114 (9.4%) were identical to a protein within ‘Hongyang’ The Kiwifruit Information Resource (KIR v2). Some proportion of the differences will be varietal polymorphisms. However, as most computationally predicted Red5 models required manual re-annotation this proportion is expected to be small. The quality of the new gene models was tested by fully sequencing 550 cloned ‘Hort16A’ cDNAs and comparing with the predicted protein models for Red5 and both the original ‘Hongyang’ assembly and the revised annotation from KIR v2. Only 48.9% and 63.5% of the cDNAs had a match with 90% identity or better to the original and revised ‘Hongyang’ annotation, respectively, compared with 90.9% to the Red5 models.

Conclusions

Our study highlights the need to take a cautious approach to draft genomes and computationally predicted genes. Our use of the manual annotation tool WebApollo facilitated manual checking and correction of gene models enabling improvement of computational prediction. This utility was especially relevant for certain types of gene families such as the EXPANSIN like genes. Finally, this high quality gene set will supply the kiwifruit and general plant community with a new tool for genomics and other comparative analysis.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4656-3) contains supplementary material, which is available to authorized users.

Correcting TG 119 confidence limits

PURPOSE

Task Group 119 (TG-119) has been adopted for evaluating the adequacy of intensity-modulated radiation therapy (IMRT) commissioning and for establishing patient-specific IMRT quality assurance (QA) passing criteria in clinical practice. TG-119 establishes 95% confidence limits (CLs), which help clinics identify systematic IMRT QA errors and identify outliers. In TG-119, the 95% CLs are established by fitting the Gamma Γ analysis passing rate results to an assumed distribution, then calculating the limit in which 95% of the data fall. CLs for a given dataset will depend greatly on the type of distribution used, and those determined by following the TG-119 guidelines are only valid if the underlying data follows a Gaussian distribution. Gaussian distributions assume symmetry about the mean, which would imply the possibility of negative Γ analysis failing rates. This study demonstrates that the gamma distribution is a more reasonable assumption for establishing CLs than the Gaussian distribution used in TG-119. Thus, the gamma distribution is suggested as a replacement to the conventional Gaussian statistical model used in TG-119.

MATERIALS AND METHODS

The moments estimator (ME) for the gamma family is used to obtain the CLs of the failing rates for all Γ analysis criteria. To demonstrate the congruence of the gamma distribution, the root mean squared error and the CL values for the MEs of the gamma and the Gaussian families were compared.

RESULTS

In this study, the empirical 95% CLs generated using 302 plans represent the ground truth, which resulted in a 91.83% passing rate using 3%/3 mm error local criteria. The gamma distribution underestimates the 95% CL by 0.09%, while the Gaussian distribution overestimates the 95% CL by 4.12%.

CONCLUSIONS

Although IMRT QA equipment may vary between clinics, the mathematical formalism presented in this study applies to any combination of planning and delivery systems. This study has demonstrated that a gamma distribution should be favored over a Gaussian distribution when establishing CLs for IMRT QA.

Commissioning and Evaluation of an Electronic Portal Imaging Device-Based In-Vivo Dosimetry Software

This study reports on our experience with the in-vivo dose verification software, EPIgray® (DOSIsoft, Cachan, France). After the initial commissioning process, clinical experiments on phantom treatments were evaluated to assess the level of accuracy of the electronic portal imaging device (EPID) based in-vivo dose verification.

EPIgray was commissioned based on the company’s instructions. This involved ion chamber measurements and portal imaging of solid water blocks of various thicknesses between 5 and 35 cm. Field sizes varied between 2 x 2 cm² and 20 x 20 cm². The determined conversion factors were adjusted through an additional iterative process using treatment planning system calculations. Subsequently, evaluation was performed using treatment plans of single and opposed beams, as well as intensity modulated radiotherapy (IMRT) plans, based on recommendations from the task group report TG-119 to test for dose reconstruction accuracy. All tests were performed using blocks of solid water slabs as a phantom.

For single square fields, the dose at isocenter was reconstructed within 3% accuracy in EPIgray compared to the treatment planning system dose. Similarly, the relative deviation of the total dose was accurately reconstructed within 3% for all IMRT plans with points placed inside a high-dose region near the isocenter. Predictions became less accurate than < 5% when the evaluation point was outside the treatment target. Dose at points 5 cm or more away from the isocenter or within an avoidance structure was reconstructed less reliably.

EPIgray formalism accuracy is adequate for an efficient error detection system with verifications performed in high-dose volumes. It provides immediate intra-fractional feedback on the delivery of treatment plans without affecting the treatment beam. Besides the EPID, no additional hardware is required. The software evaluates local point dose measurements to verify treatment plan delivery and patient positioning within 5% accuracy, depending on the placement of evaluation points.

Osteochondritis dissecans of the humeral capitellum: reliability of four classification systems using radiographs and computed tomography

BACKGROUND

The radiographic appearance of osteochondritis dissecans (OCD) of the humeral capitellum varies according to the stage of the lesion. It is important to evaluate the stage of OCD lesion carefully to guide treatment. We compared the interobserver reliability of currently used classification systems for OCD of the humeral capitellum to identify the most reliable classification system.

METHODS

Thirty-two musculoskeletal radiologists and orthopaedic surgeons specialized in elbow surgery from several countries evaluated anteroposterior and lateral radiographs and corresponding computed tomography (CT) scans of 22 patients to classify the stage of OCD of the humeral capitellum according to the classification systems developed by (1) Minami, (2) Berndt and Harty, (3) Ferkel and Sgaglione, and (4) Anderson on a Web-based study platform including a Digital Imaging and Communications in Medicine viewer. Magnetic resonance imaging was not evaluated as part of this study. We measured agreement among observers using the Siegel and Castellan multirater κ.

RESULTS

All OCD classification systems, except for Berndt and Harty, which had poor agreement among observers (κ = 0.20), had fair interobserver agreement: κ was 0.27 for the Minami, 0.23 for Anderson, and 0.22 for Ferkel and Sgaglione classifications. The Minami Classification was significantly more reliable than the other classifications (P < .001).

CONCLUSIONS

The Minami Classification was the most reliable for classifying different stages of OCD of the humeral capitellum. However, it is unclear whether radiographic evidence of OCD of the humeral capitellum, as categorized by the Minami Classification, guides treatment in clinical practice as a result of this fair agreement.

Fast range-corrected proton dose approximation method using prior dose distribution

For robust plan optimization and evaluation purposes, one needs a computationally efficient way to calculate dose distributions and dose-volume histograms (DVHs) under various changes in the variables associated with beam delivery and images. In this study, we report an approximate method for rapid calculation of dose when setup errors and anatomical changes occur during proton therapy. This fast dose approximation method calculates new dose distributions under various circumstances based on the prior knowledge of dose distribution from a reference setting. In order to validate the method, we calculated and compared the dose distributions from our approximation method to the dose distributions calculated from a clinically commissioned treatment planning system which was used as the ground truth. The overall accuracy of the proposed method was tested against varying degrees of setup error and anatomical deformation for selected patient cases. The setup error was simulated by rigid shifts of the patient; while the anatomical deformation was introduced using weekly acquired repeat CT data sets. We evaluated the agreement between the dose approximation method and full dose recalculation using a 3D gamma index and the root-mean-square (RMS) and maximum deviation of the cumulative dose volume histograms (cDVHs). The average passing rate of 3D gamma analysis under 3% dose and 3 mm distance-to-agreement criteria were 96% and 89% for setup errors and severe anatomy changes, respectively. The average of RMS and maximum deviation of the cDVHs under the setup error was 0.5% and 1.5%, respectively for all structures considered. Similarly, the average of RMS and maximum deviations under the weekly anatomical change were 0.6% and 2.7%, respectively. Our results show that the fast dose approximation method was able to account for the density variation of the patient due to the setup and anatomical changes with acceptable accuracy while significantly improving the computation time.

Association of bone turnover markers and arterial stiffness in pre-dialysis chronic kidney disease (CKD)

Vascular calcification (VC) is highly prevalent in CKD and leads to increased vascular stiffness and cardiovascular disease (CVD). Non-traditional cardiovascular risk factors include abnormal bone turnover and/or dysregulation of the calcification inhibitors, although their relative contribution remains unclear. We investigated the association between bone turnover, the calcification inhibitors (matrix gla protein; MGP and Fetuin-A), and the phosphate regulating hormone; fibroblast growth factor-23 (FGF-23) and arterial stiffness in pre-dialysis CKD patients. One hundred and forty-five patients with CKD stages 1-4 (74 M, 71 F) aged (mean [SD]) 53 [14] years were studied. Bone turnover markers (bone-specific alkaline phosphatase (BALP) and tartrate-resistant acid phosphatase (TRACP)) and MGP, Fetuin-A and FGF-23 were determined. BMD was measured at the lumbar spine (LS), femoral neck (FN), forearm (FARM) and total hip (TH). Arterial stiffness was assessed by contour analysis of digital volume pulse (SI(DVP)). There was a significant positive correlation between TRACP:BALP ratio and SI(DVP) ( r=0.19, p=0.023). Following multi-linear regression analysis, significant associations were seen between serum BALP (p=0.037), TRACP (p=0.009) and TRACP:BALP ratio (p=0.001) and SI(DVP) independently of traditional CVD risk factors. No significant relationship between SI(DVP) and MGP, Fetuin-A and FGF-23 was observed. A significant negative correlation was seen between BMD at the FARM and SI(DVP) in CKD stage 4 (r=-0.35, p=0.024). The association remained significant following correction for age, gender and cardiovascular risk factors (p=0.029). Our data suggest a link between imbalances in bone turnover and arterial stiffness in pre-dialysis CKD. Longitudinal studies are needed to evaluate the clinical usefulness of these bone turnover markers as predictors of CVD in CKD.

Dose perturbations and image artifacts caused by carbon-coated ceramic and stainless steel fiducials used in proton therapy for prostate cancer

Image-guided radiation therapy using implanted fiducial markers is a common solution for prostate localization to improve targeting accuracy. However, fiducials that are typically used for conventional photon radiotherapy cause large dose perturbations in patients who receive proton radiotherapy. A proposed solution has been to use fiducials of lower atomic number (Z) materials to minimize this effect in tissue, but the effects of these fiducials on dose distributions have not been quantified. The objective of this study was to analyze the magnitude of the dose perturbations caused by select lower-Z fiducials (a carbon-coated zirconium dioxide fiducial and a plastic-coated stainless steel fiducial) and compare them to perturbations caused by conventional gold fiducials. Sets of phantoms were used to assess select components of the effects on dose. First, the fiducials were assessed for radiographic visibility using both conventional computed tomography (CT) and an on-board kilovoltage imaging device at our proton therapy center. CT streak artifacts from the fiducials were also measured in a separate phantom. Second, dose perturbations were measured downstream of the fiducials using radiochromic film. The magnitude of dose perturbation was characterized as a function of marker material, implantation depth and orientation with respect to the beam axis. The radiographic visibility of the markers was deemed to be acceptable for clinical use. The dose measurements showed that the perpendicularly oriented zirconium dioxide and stainless steel fiducials located near the center of modulation of the proton beam perturbed the dose by less than 10%, but that the same fiducials in a parallel orientation near the end of the range of the beam could perturb the dose by as much as 38%. This suggests that carbon-coated and stainless steel fiducials could be used in proton therapy if they are located far from the end of the range of the beam and if they are oriented perpendicular to the beam axis.

Circulating fibroblast growth factor-23 increases following intermittent parathyroid hormone (1-34) in postmenopausal osteoporosis: association with biomarker of bone formation

Uncertainties exist regarding whether FGF-23 production is influenced by PTH and its involvement in bone formation. We evaluated FGF-23 response and its relation to changes in biomarkers of bone formation following intermittent PTH treatment. Twenty-seven women with a mean [SD] age of 75.8 [5.4] years with postmenopausal osteoporosis were treated with PTH(1-34) for 18 months. Bone mineral density (BMD) was measured at 6 and 18 months at the lumbar spine (LS) and total hip (TH). Blood samples were obtained at baseline, 1-3, 6-9, and 12-18 months. Serum calcium, phosphate, PTH, 25(OH)vitamin D, 1,25(OH)(2)vitamin D, markers of bone turnover, FGF-23, and sclerostin were measured. BMD increased at both the LS (11.6%, P < 0.001) and TH (2.5%, P < 0.01). The bone formation marker P1NP increased early (baseline mean [SD] 39.9 [24.4] μg/l, 1-3 months 88 [37.9] μg/l; P < 0.001) and remained higher than baseline throughout 18 months. FGF-23 also increased, with a peak response at 6-9 months (increase 65%, P = 0.002). Serum phosphate remained stable. A significant increase in 1.25(OH)(2)vitamin D (P = 0.02) was seen at 1-3 months only. A small but significant reduction in sclerostin was seen at 6-9 (P = 0.02) and 12-18 months (P = 0.06). There was a positive correlation between changes in P1NP and FGF-23 (6-9 months r = 0.78, P < 0.001). FGF-23 is increased by intermittent PTH(1-34). This is related to early changes in P1NP, suggesting that the skeletal effects of PTH may involve FGF-23. Further studies are required to elucidate this.