Robotic-Assisted Esophagectomy Leads to Significant Reduction in Postoperative Acute Pain: A Retrospective Clinical Trial.

BACKGROUND: Robot-assisted minimally invasive esophagectomy (RAMIE) shows promising results regarding postoperative complications in patients with esophageal cancer. To date, no data are available regarding postoperative analgesic consumption. The aim of this work is to evaluate analgesic consumption after esophagectomy. METHODS: A total of 274 Ivor Lewis esophageal resections performed sequentially from January 2012 to December 2020 were evaluated. RAMIE cases (n = 51) were compared with the hybrid technique (laparoscopic abdominal phase followed by open thoracotomy, n = 59) and open abdominothoracic esophagectomy (OTE) (n = 164). Data were collected retrospectively. The primary endpoint was the overall postoperative morphine consumption, which represents a reliable indirect measurement of pain. Pain levels recorded on the first, third, and fifth postoperative days were assessed as secondary endpoints. RESULTS: A total of 274 patients were included. The postoperative opioid consumption rate for patients who underwent RAMIE (quartiles: 0.14, 0.23, 0.36 mg morphine milligram equivalents (MME)/kg body weight (bw)/day) was significantly lower than in the open group (0.19, 0.33, 0.58 mg MME/kg bw/day, p = 0.016). The overall postoperative opioid consumption for patients who underwent RAMIE was significantly lower (2.45, 3.63, 7.20 mg MME/kg bw/day; morphine milligram equivalents per kilogram body weight) compared with the open (4.85, 8.59, 14.63 MME/kg bw/day, p < 0.0001) and hybrid (4.13, 6.84, 11.36 MME/kg bw/day, p = 0.008) groups. Patients who underwent RAMIE reported lower pain scores compared with the open group on the fifth postoperative day, both at rest (p = 0.004) and while performing activities (p < 0.001). CONCLUSIONS: This study shows that patients who underwent RAMIE experienced similar postoperative pain while requiring significantly lower amounts of opioids compared with patients who underwent open and hybrid surgery. Further studies are required to verify the results.

Interpretable Classification of Bacterial Raman Spectra With Knockoff Wavelets.

Deep neural networks and other machine learning models are widely applied to biomedical signal data because they can detect complex patterns and compute accurate predictions. However, the difficulty of interpreting such models is a limitation, especially for applications involving high-stakes decision, including the identification of bacterial infections. This paper considers fast Raman spectroscopy data and demonstrates that a logistic regression model with carefully selected features achieves accuracy comparable to that of neural networks, while being much simpler and more transparent. Our analysis leverages wavelet features with intuitive chemical interpretations, and performs controlled variable selection with knockoffs to ensure the predictors are relevant and non-redundant. Although we focus on a particular data set, the proposed approach is broadly applicable to other types of signal data for which interpretability may be important.

False discovery rate control in genome-wide association studies with population structure.

We present a comprehensive statistical framework to analyze data from genome-wide association studies of polygenic traits, producing interpretable findings while controlling the false discovery rate. In contrast with standard approaches, our method can leverage sophisticated multivariate algorithms but makes no parametric assumptions about the unknown relation between genotypes and phenotype. Instead, we recognize that genotypes can be considered as a random sample from an appropriate model, encapsulating our knowledge of genetic inheritance and human populations. This allows the generation of imperfect copies (knockoffs) of these variables that serve as ideal negative controls, correcting for linkage disequilibrium and accounting for unknown population structure, which may be due to diverse ancestries or familial relatedness. The validity and effectiveness of our method are demonstrated by extensive simulations and by applications to the UK Biobank data. These analyses confirm our method is powerful relative to state-of-the-art alternatives, while comparisons with other studies validate most of our discoveries. Finally, fast software is made available for researchers to analyze Biobank-scale datasets.

Causal inference in genetic trio studies.

We introduce a method to draw causal inferences-inferences immune to all possible confounding-from genetic data that include parents and offspring. Causal conclusions are possible with these data because the natural randomness in meiosis can be viewed as a high-dimensional randomized experiment. We make this observation actionable by developing a conditional independence test that identifies regions of the genome containing distinct causal variants. The proposed digital twin test compares an observed offspring to carefully constructed synthetic offspring from the same parents to determine statistical significance, and it can leverage any black-box multivariate model and additional nontrio genetic data to increase power. Crucially, our inferences are based only on a well-established mathematical model of recombination and make no assumptions about the relationship between the genotypes and phenotypes. We compare our method to the widely used transmission disequilibrium test and demonstrate enhanced power and localization.

Publisher Correction: Multi-resolution localization of causal variants across the genome.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Multi-resolution localization of causal variants across the genome.

In the statistical analysis of genome-wide association data, it is challenging to precisely localize the variants that affect complex traits, due to linkage disequilibrium, and to maximize power while limiting spurious findings. Here we report on KnockoffZoom: a flexible method that localizes causal variants at multiple resolutions by testing the conditional associations of genetic segments of decreasing width, while provably controlling the false discovery rate. Our method utilizes artificial genotypes as negative controls and is equally valid for quantitative and binary phenotypes, without requiring any assumptions about their genetic architectures. Instead, we rely on well-established genetic models of linkage disequilibrium. We demonstrate that our method can detect more associations than mixed effects models and achieve fine-mapping precision, at comparable computational cost. Lastly, we apply KnockoffZoom to data from 350k subjects in the UK Biobank and report many new findings.