Evaluating Prognostic Bias of Critical Illness Severity Scores Based on Age, Sex, and Primary Language in the United States: A Retrospective Multicenter Study.

OBJECTIVES: Although illness severity scoring systems are widely used to support clinical decision-making and assess ICU performance, their potential bias across different age, sex, and primary language groups has not been well-studied. DESIGN SETTING AND PATIENTS: We aimed to identify potential bias of Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) IVa scores via large ICU databases. SETTING/PATIENTS: This multicenter, retrospective study was conducted using data from the Medical Information Mart for Intensive Care (MIMIC) and eICU Collaborative Research Database. SOFA and APACHE IVa scores were obtained from ICU admission. Hospital mortality was the primary outcome. Discrimination (area under receiver operating characteristic [AUROC] curve) and calibration (standardized mortality ratio [SMR]) were assessed for all subgroups. INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: A total of 196,310 patient encounters were studied. Discrimination for both scores was worse in older patients compared with younger patients and female patients rather than male patients. In MIMIC, discrimination of SOFA in non-English primary language speakers patients was worse than that of English speakers (AUROC 0.726 vs. 0.783, p < 0.0001). Evaluating calibration via SMR showed statistically significant underestimations of mortality when compared with overall cohort in the oldest patients for both SOFA and APACHE IVa, female patients (1.09) for SOFA, and non-English primary language patients (1.38) for SOFA in MIMIC. CONCLUSIONS: Differences in discrimination and calibration of two scores across varying age, sex, and primary language groups suggest illness severity scores are prone to bias in mortality predictions. Caution must be taken when using them for quality benchmarking and decision-making among diverse real-world populations.

Base editing rescue of spinal muscular atrophy in cells and in mice.

Spinal muscular atrophy (SMA), the leading genetic cause of infant mortality, arises from survival motor neuron (SMN) protein insufficiency resulting from SMN1 loss. Approved therapies circumvent endogenous SMN regulation and require repeated dosing or may wane. We describe genome editing of SMN2, an insufficient copy of SMN1 harboring a C6>T mutation, to permanently restore SMN protein levels and rescue SMA phenotypes. We used nucleases or base editors to modify five SMN2 regulatory regions. Base editing converted SMN2 T6>C, restoring SMN protein levels to wild type. Adeno-associated virus serotype 9-mediated base editor delivery in Δ7SMA mice yielded 87% average T6>C conversion, improved motor function, and extended average life span, which was enhanced by one-time base editor and nusinersen coadministration (111 versus 17 days untreated). These findings demonstrate the potential of a one-time base editing treatment for SMA.

Generating experimentally unrelated target molecule-binding highly functionalized nucleic-acid polymers using machine learning.

In vitro selection queries large combinatorial libraries for sequence-defined polymers with target binding and reaction catalysis activity. While the total sequence space of these libraries can extend beyond 1022 sequences, practical considerations limit starting sequences to ≤~1015 distinct molecules. Selection-induced sequence convergence and limited sequencing depth further constrain experimentally observable sequence space. To address these limitations, we integrate experimental and machine learning approaches to explore regions of sequence space unrelated to experimentally derived variants. We perform in vitro selections to discover highly side-chain-functionalized nucleic acid polymers (HFNAPs) with potent affinities for a target small molecule (daunomycin KD = 5-65 nM). We then use the selection data to train a conditional variational autoencoder (CVAE) machine learning model to generate diverse and unique HFNAP sequences with high daunomycin affinities (KD = 9-26 nM), even though they are unrelated in sequence to experimental polymers. Coupling in vitro selection with a machine learning model thus enables direct generation of active variants, demonstrating a new approach to the discovery of functional biopolymers.

Peptide fusion improves prime editing efficiency.

Prime editing enables search-and-replace genome editing but is limited by low editing efficiency. We present a high-throughput approach, the Peptide Self-Editing sequencing assay (PepSEq), to measure how fusion of 12,000 85-amino acid peptides influences prime editing efficiency. We show that peptide fusion can enhance prime editing, prime-enhancing peptides combine productively, and a top dual peptide-prime editor increases prime editing significantly in multiple cell lines across dozens of target sites. Top prime-enhancing peptides function by increasing translation efficiency and serve as broadly useful tools to improve prime editing efficiency.

Meta-Analysis of Point-of-Care Lung Ultrasonography Versus Chest Radiography in Adults With Symptoms of Acute Decompensated Heart Failure.

Acute decompensated heart failure (ADHF) is a primary cause of older adults presenting to the emergency department with acute dyspnea. Point-of-care lung ultrasound (LUS) has shown comparable or superior diagnostic accuracy in comparison with a chest x-ray (CXR) in patients presenting with symptoms of ADHF. The systematic review and meta-analysis aimed to elucidate the sensitivity and specificity of LUS in comparison with CXR for diagnosing ADHF and summarize the rapidly growing body of evidence in this domain. A total of 5 databases were searched through February 18, 2021, to identify observational studies that reported on the use of LUS compared with CXR in diagnosing ADHF in patients presenting with shortness of breath. Meta-analysis was conducted on the sensitivities and specificities of each diagnostic method. A total of 8 studies reporting on 2,787 patients were included in this meta-analysis. For patients presenting with signs and symptoms of ADHF, LUS was found to be more sensitive than CXR (91.8% vs 76.5%) and more specific than CXR (92.3% vs 87.0%) for the detection of cardiogenic pulmonary edema. In conclusion, LUS is more sensitive and specific than CXR in detecting pulmonary edema. This highlights the importance of sonographic B-lines, along with the accurate interpretation of clinical data, in the diagnosis of ADHF. In addition to its convenience, reduced costs, and reduced radiation exposure, LUS should be considered an effective alternative to CXR for evaluating patients with dyspnea in the setting of ADHF.

Vibration-controlled Transient Elastography for Assessment of Liver Fibrosis at a USA Academic Medical Center.

Background and Aims: Vibration-controlled transient elastography (VCTE) is a noninvasive tool that uses liver stiffness measurement (LSM) to assess fibrosis. Since real-life data during everyday clinical practice in the USA are lacking, we describe the patterns of use and diagnostic performance of VCTE in patients at an academic medical center in New York City. Methods: Patients who received VCTE scans were included if liver biopsy was performed within 1 year. Diagnostic performance of VCTE in differentiating dichotomized fibrosis stages was assessed via area under the receiver operating characteristics (AUROC). Fibrosis stage determined from VCTE LSM was compared to liver biopsy. Results: Of 109 patients, 49 had nonalcoholic fatty liver disease, 16 chronic hepatitis C, 15 congestive hepatopathy, and 22 at least two etiologies. AUROC was 0.90 for differentiating cirrhosis (stage 4) with a positive predictive value (PPV) range of 0.28 to 0.45 and negative predictive value range of 0.96 to 0.98. For 31 (32%) patients, VCTE fibrosis stage was at least two stages higher than liver biopsy fibrosis stage. Thirteen of thirty-five patients considered to have cirrhosis by VCTE had stage 0 to 2 and 12 stage 3 fibrosis on liver biopsy. Conclusions: VCTE has reasonable diagnostic accuracy and is reliable at ruling out cirrhosis. However, because of its low PPV, caution must be exercised when used to diagnose cirrhosis, as misdiagnosis can lead to unnecessary health care interventions. In routine practice, VTCE is also sometimes performed for disease etiologies for which it has not been robustly validated.

Effect of malnutrition on outcomes in patients with heart failure: A large retrospective propensity score-matched cohort study.

BACKGROUND: Heart failure (HF) is highly prevalent, whereas malnutrition is generally associated with poorer hospital outcomes, and it is not uncommon in patients with HF. Prior studies of the effect of malnutrition on HF outcomes are limited in size and quality. This study aims to elucidate the association between malnutrition and hospital length of stay (LOS), mortality, and discharge destination in patients with HF. METHODS: This is a retrospective review of medical records for inpatients admitted with a primary diagnosis of HF in 2018. Patients with HF and severe protein-calorie malnutrition were compared with those without malnutrition. A two-sided t-test was conducted between patients who have HF with and without malnutrition on hospital outcomes. Multivariate logistic regression was developed to identify potential predictors of malnutrition. A propensity score was calculated for each patient and matched cases (malnutrition with nonmalnutrition) to balance covariates and reduce bias. RESULTS: For N = 7079, the median age was 75 years, with 15.79% having severe malnutrition. Overall mortality was 5.57% (394 deceased) . There were significant associations between malnutrition and both mortality (relative risk, 2.22; P < 0.001) and LOS (10 vs 5 days, P < 0.001) in patients with HF. Significantly fewer patients with malnutrition were discharged home (odds ratio, 0.41; P < 0.001). CONCLUSION: Patients with HF and malnutrition have higher risk for mortality, increased LOS in the hospital, and decreased chance of being discharged home. Continued study of this population is required to better predict which patients with malnutrition will respond to nutrition interventions.

Colchicine use in patients with COVID-19: A systematic review and meta-analysis.

INTRODUCTION: Colchicine may inhibit inflammasome signaling and reduce proinflammatory cytokines, a purported mechanism of COVID-19 pneumonia. The aim of this systematic review and meta-analysis is to report on the state of the current literature on the use of colchicine in COVID-19 and to investigate the reported clinical outcomes in COVID-19 patients by colchicine usage. METHODS: The literature was searched from January 2019 through January 28, 2021. References were screened to identify studies that reported the effect of colchicine usage on COVID-19 outcomes including mortality, intensive care unit (ICU) admissions, or mechanical ventilation. Studies were meta-analyzed for mortality by the subgroup of trial design (RCT vs observational) and ICU status. Studies reporting an risk ratio (RR), odds ratio (OR) and hazard ratio (HR) were analyzed separately. RESULTS: Eight studies, reporting on 16,248 patients, were included in this review. The Recovery trial reported equivalent mortality between colchicine and non-colchicine users. Across the other studies, patients who received colchicine had a lower risk of mortality-HR of 0.25 (95% CI: 0.09, 0.66) and OR of 0.22 (95% CI: 0.09, 0.57). There was no statistical difference in risk of ICU admissions between patients with COVID-19 who received colchicine and those who did not-OR of 0.26 (95% CI: 0.06, 1.09). CONCLUSION: Colchicine may reduce the risk of mortality in individuals with COVID-19. Further prospective investigation may further determine the efficacy of colchicine as treatment in COVID-19 patients in various care settings of the disease, including post-hospitalization and long-term care.

Effect of famotidine on hospitalized patients with COVID-19: A systematic review and meta-analysis.

INTRODUCTION: Famotidine is a competitive histamine H2-receptor antagonist most commonly used for gastric acid suppression but thought to have potential efficacy in treating patients with Coronavirus disease 2019 (COVID-19). The aims of this systematic review and meta-analysis are to summarize the current literature and report clinical outcomes on the use of famotidine for treatment of hospitalized patients with COVID-19. METHODS: Five databases were searched through February 12, 2021 to identify observational studies that reported on associations of famotidine use with outcomes in COVID-19. Meta-analysis was conducted for composite primary clinical outcome (e.g. rate of death, intubation, or intensive care unit admissions) and death separately, where either aggregate odds ratio (OR) or hazard ratio (HR) was calculated. RESULTS: Four studies, reporting on 46,435 total patients and 3,110 patients treated with famotidine, were included in this meta-analysis. There was no significant association between famotidine use and composite outcomes in patients with COVID-19: HR 0.63 (95% CI: 0.35, 1.16). Across the three studies that reported mortality separated from other endpoints, there was no association between famotidine use during hospitalization and risk of death-HR 0.67 (95% CI: 0.26, 1.73) and OR 0.79 (95% CI: 0.19, 3.34). Heterogeneity ranged from 83.69% to 88.07%. CONCLUSION: Based on the existing observational studies, famotidine use is not associated with a reduced risk of mortality or combined outcome of mortality, intubation, and/or intensive care services in hospitalized individuals with COVID-19, though heterogeneity was high, and point estimates suggested a possible protective effect for the composite outcome that may not have been observed due to lack of power. Further randomized controlled trials (RCTs) may help determine the efficacy and safety of famotidine as a treatment for COVID-19 patients in various care settings of the disease.

Reconstruction of evolving gene variants and fitness from short sequencing reads.

Directed evolution can generate proteins with tailor-made activities. However, full-length genotypes, their frequencies and fitnesses are difficult to measure for evolving gene-length biomolecules using most high-throughput DNA sequencing methods, as short read lengths can lose mutation linkages in haplotypes. Here we present Evoracle, a machine learning method that accurately reconstructs full-length genotypes (R2 = 0.94) and fitness using short-read data from directed evolution experiments, with substantial improvements over related methods. We validate Evoracle on phage-assisted continuous evolution (PACE) and phage-assisted non-continuous evolution (PANCE) of adenine base editors and OrthoRep evolution of drug-resistant enzymes. Evoracle retains strong performance (R2 = 0.86) on data with complete linkage loss between neighboring nucleotides and large measurement noise, such as pooled Sanger sequencing data (~US$10 per timepoint), and broadens the accessibility of training machine learning models on gene variant fitnesses. Evoracle can also identify high-fitness variants, including low-frequency 'rising stars', well before they are identifiable from consensus mutations.

Small molecule inhibition of ATM kinase increases CRISPR-Cas9 1-bp insertion frequency.

Mutational outcomes following CRISPR-Cas9-nuclease cutting in mammalian cells have recently been shown to be predictable and, in certain cases, skewed toward single genotypes. However, the ability to control these outcomes remains limited, especially for 1-bp insertions, a common and therapeutically relevant class of repair outcomes. Here, through a small molecule screen, we identify the ATM kinase inhibitor KU-60019 as a compound capable of reproducibly increasing the fraction of 1-bp insertions relative to other Cas9 repair outcomes. Small molecule or genetic ATM inhibition increases 1-bp insertion outcome fraction across three human and mouse cell lines, two Cas9 species, and dozens of target sites, although concomitantly reducing the fraction of edited alleles. Notably, KU-60019 increases the relative frequency of 1-bp insertions to over 80% of edited alleles at several native human genomic loci and improves the efficiency of correction for pathogenic 1-bp deletion variants. The ability to increase 1-bp insertion frequency adds another dimension to precise template-free Cas9-nuclease genome editing.

Neurology trial registrations on ClinicalTrials.gov between 2007 and 2018: A cross-sectional analysis of characteristics, early discontinuation, and results reporting.

BACKGROUND: Increasing neurological disease burden and advancing treatment options require clinical trials to expand the evidence base of clinical care. We aimed to characterize neurology clinical trials registered between October 2007 and April 2018 and identify features associated with early discontinuation and results reporting. METHODS: We compared 16,994 neurology (9.4%) and 163,714 non-neurology comparison trials registered to ClinicalTrials.gov. Trials therapeutic focus within neurology was assigned via combination programmatic and manual review. We performed descriptive analyses of trial characteristics, cox regression of early discontinuation, and multivariable logistic regression for results reporting within 3 years of completion. RESULTS: Most neurology trials were academic-funded (58.5%) followed by industry (31.9%) and US-government (9.6%). Neurology trials focused more on treatment than prevention compared to non-neurology studies. Of neurology trials, 11.3% discontinued early, and 32.2% of completed trials reported results by April 30, 2018. In multivariable analysis accounting for time-to-event, neurology trials were at lower risk of discontinuation than non-neurology trials (adjusted hazard 0.83, p < 0.0001). Both academic and government-funded trials had greater risk of discontinuation than industry (adjusted hazard 0.57 and 0.46, respectively). Among completed trials, government-funded studies (adjusted odds ratio 2.12, p < 0.0001) had highest odds of results reporting while academic trials reported less (adjusted odds ratio 0.51, p < 0.0001). CONCLUSIONS: Funding source is associated with trial characteristics and outcomes in neurology. Improvements in trial completion and timely dissemination of results remain urgent goals for the field.

Efficient C•G-to-G•C base editors developed using CRISPRi screens, target-library analysis, and machine learning.

Programmable C•G-to-G•C base editors (CGBEs) have broad scientific and therapeutic potential, but their editing outcomes have proved difficult to predict and their editing efficiency and product purity are often low. We describe a suite of engineered CGBEs paired with machine learning models to enable efficient, high-purity C•G-to-G•C base editing. We performed a CRISPR interference (CRISPRi) screen targeting DNA repair genes to identify factors that affect C•G-to-G•C editing outcomes and used these insights to develop CGBEs with diverse editing profiles. We characterized ten promising CGBEs on a library of 10,638 genomically integrated target sites in mammalian cells and trained machine learning models that accurately predict the purity and yield of editing outcomes (R = 0.90) using these data. These CGBEs enable correction to the wild-type coding sequence of 546 disease-related transversion single-nucleotide variants (SNVs) with >90% precision (mean 96%) and up to 70% efficiency (mean 14%). Computational prediction of optimal CGBE-single-guide RNA pairs enables high-purity transversion base editing at over fourfold more target sites than achieved using any single CGBE variant.

Detection of gene cis-regulatory element perturbations in single-cell transcriptomes.

We introduce poly-adenine CRISPR gRNA-based single-cell RNA-sequencing (pAC-Seq), a method that enables the direct observation of guide RNAs (gRNAs) in scRNA-seq. We use pAC-Seq to assess the phenotypic consequences of CRISPR/Cas9 based alterations of gene cis-regulatory regions. We show that pAC-Seq is able to detect cis-regulatory-induced alteration of target gene expression even when biallelic loss of target gene expression occurs in only ~5% of cells. This low rate of biallelic loss significantly increases the number of cells required to detect the consequences of changes to the regulatory genome, but can be ameliorated by transcript-targeted sequencing. Based on our experimental results we model the power to detect regulatory genome induced transcriptomic effects based on the rate of mono/biallelic loss, baseline gene expression, and the number of cells per target gRNA.

PrimeDesign software for rapid and simplified design of prime editing guide RNAs.

Prime editing (PE) is a versatile genome editing technology, but design of the required guide RNAs is more complex than for standard CRISPR-based nucleases or base editors. Here we describe PrimeDesign, a user-friendly, end-to-end web application and command-line tool for the design of PE experiments. PrimeDesign can be used for single and combination editing applications, as well as genome-wide and saturation mutagenesis screens. Using PrimeDesign, we construct PrimeVar, a comprehensive and searchable database that includes candidate prime editing guide RNA (pegRNA) and nicking sgRNA (ngRNA) combinations for installing or correcting >68,500 pathogenic human genetic variants from the ClinVar database. Finally, we use PrimeDesign to design pegRNAs/ngRNAs to install a variety of human pathogenic variants in human cells.

Machine learning based CRISPR gRNA design for therapeutic exon skipping.

Restoring gene function by the induced skipping of deleterious exons has been shown to be effective for treating genetic disorders. However, many of the clinically successful therapies for exon skipping are transient oligonucleotide-based treatments that require frequent dosing. CRISPR-Cas9 based genome editing that causes exon skipping is a promising therapeutic modality that may offer permanent alleviation of genetic disease. We show that machine learning can select Cas9 guide RNAs that disrupt splice acceptors and cause the skipping of targeted exons. We experimentally measured the exon skipping frequencies of a diverse genome-integrated library of 791 splice sequences targeted by 1,063 guide RNAs in mouse embryonic stem cells. We found that our method, SkipGuide, is able to identify effective guide RNAs with a precision of 0.68 (50% threshold predicted exon skipping frequency) and 0.93 (70% threshold predicted exon skipping frequency). We anticipate that SkipGuide will be useful for selecting guide RNA candidates for evaluation of CRISPR-Cas9-mediated exon skipping therapy.

Comprehensive Mapping of Key Regulatory Networks that Drive Oncogene Expression.

Gene expression is controlled by the collective binding of transcription factors to cis-regulatory regions. Deciphering gene-centered regulatory networks is vital to understanding and controlling gene misexpression in human disease; however, systematic approaches to uncovering regulatory networks have been lacking. Here we present high-throughput interrogation of gene-centered activation networks (HIGAN), a pipeline that employs a suite of multifaceted genomic approaches to connect upstream signaling inputs, trans-acting TFs, and cis-regulatory elements. We apply HIGAN to understand the aberrant activation of the cytidine deaminase APOBEC3B, an intrinsic source of cancer hypermutation. We reveal that nuclear factor κB (NF-κB) and AP-1 pathways are the most salient trans-acting inputs, with minor roles for other inflammatory pathways. We identify a cis-regulatory architecture dominated by a major intronic enhancer that requires coordinated NF-κB and AP-1 activity with secondary inputs from distal regulatory regions. Our data demonstrate how integration of cis and trans genomic screening platforms provides a paradigm for building gene-centered regulatory networks.