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As the biomedical community produces datasets that are increasingly complex and high dimensional, there is a need for more sophisticated computational tools to extract biological insights. We present Multiscale PHATE, a method that sweeps through all levels of data granularity to learn abstracted biological features directly predictive of disease outcome. Built on a coarse-graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse resolutions for high-level summarizations of data and at fine resolutions for detailed representations of subsets. We apply Multiscale PHATE to a coronavirus disease 2019 (COVID-19) dataset with 54 million cells from 168 hospitalized patients and find that patients who die show CD16(hi)CD66b(lo) neutrophil and IFN-γ(+) granzyme B(+) Th17 cell responses. We also show that population groupings from Multiscale PHATE directly fed into a classifier predict disease outcome more accurately than naive featurizations of the data. Multiscale PHATE is broadly generalizable to different data types, including flow cytometry, single-cell RNA sequencing (scRNA-seq), single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), and clinical variables.

Acute gastrointestinal bleeding is the most common gastrointestinal cause for hospitalization. For high-risk patients requiring intensive care unit stay, predicting transfusion needs during the first 24 h using dynamic risk assessment may improve resuscitation with red blood cell transfusion in admitted patients with severe acute gastrointestinal bleeding. A patient cohort admitted for acute gastrointestinal bleeding (N = 2,524) was identified from the Medical Information Mart for Intensive Care III (MIMIC-III) critical care database and separated into training (N = 2,032) and internal validation (N = 492) sets. The external validation patient cohort was identified from the eICU collaborative database of patients admitted for acute gastrointestinal bleeding presenting to large urban hospitals (N = 1,526). 62 demographic, clinical, and laboratory test features were consolidated into 4-h time intervals over the first 24 h from admission. The outcome measure was the transfusion of red blood cells during each 4-h time interval. A long short-term memory (LSTM) model, a type of Recurrent Neural Network, was compared to a regression-based models on time-updated data. The LSTM model performed better than discrete time regression-based models for both internal validation (AUROC 0.81 vs 0.75 vs 0.75; P ) and external validation (AUROC 0.65 vs 0.56 vs 0.56; P ). A LSTM model can be used to predict the need for transfusion of packed red blood cells over the first 24 h from admission to help personalize the care of high-risk patients with acute gastrointestinal bleeding.

The future of gastrointestinal bleeding will include the integration of machine learning algorithms to enhance clinician risk assessment and decision making. Machine learning algorithms have shown promise in outperforming existing clinical risk scores for both upper and lower gastrointestinal bleeding but have not been validated in any prospective clinical trials. The adoption of electronic health records provides an exciting opportunity to deploy risk prediction tools in real time and also to expand the data available to train predictive models. Machine learning algorithms can be used to identify patients with acute gastrointestinal bleeding using data extracted from the electronic health record. This can lead to an automated process to find patients with symptoms of acute gastrointestinal bleeding so that risk prediction tools can be then triggered to consistently provide decision support to the physician. Neural network models can be used to provide continuous risk predictions for patients who are at higher risk, which can be used to guide triage of patients to appropriate levels of care. Finally, the future will likely include neural network-based analysis of endoscopic stigmata of bleeding to help guide best practices for hemostasis during the endoscopic procedure. Machine learning will enhance the delivery of care at every level for patients with acute gastrointestinal bleeding through identifying very low risk patients for outpatient management, triaging high risk patients for higher levels of care, and guiding optimal intervention during endoscopy.

BACKGROUND AND AIM Guidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time within electronic health records (EHRs) would overcome a major impediment. This requires an automated mechanism to accurately identify ("phenotype") patients with GIB at the time of presentation. The goal is to identify patients with acute GIB by developing and evaluating EHR-based phenotyping algorithms for emergency department (ED) patients. METHODS We specified criteria using structured data elements to create rules for identifying patients and also developed multiple natural language processing (NLP)-based approaches for automated phenotyping of patients, tested them with tenfold cross-validation for 10 iterations (n = 7144) and external validation (n = 2988) and compared them with a standard method to identify patient conditions, the Systematized Nomenclature of Medicine. The gold standard for GIB diagnosis was the independent dual manual review of medical records. The primary outcome was the positive predictive value. RESULTS A decision rule using GIB-specific terms from ED triage and ED review-of-systems assessment performed better than the Systematized Nomenclature of Medicine on internal validation and external validation (positive predictive value = 85% confidence interval:83%-87% vs 69% confidence interval:66%-72%; P

Artificial intelligence may improve value in colonoscopy-based colorectal screening and surveillance by improving quality and decreasing unnecessary costs. The quality of screening and surveillance as measured by adenoma detection rates can be improved through real-time computer-assisted detection of polyps. Unnecessary costs can be decreased with optical biopsies to identify low-risk polyps using computer-assisted diagnosis that can undergo the resect-and-discard or diagnose-and-leave strategy. Key challenges include the clinical integration of artificial intelligence-based technology into the endoscopists' workflow, the effect of this technology on endoscopy center efficiency, and the interpretability of the underlying deep learning algorithms. The future for image-based artificial intelligence in gastroenterology will include applications to improve the diagnosis and treatment of cancers throughout the gastrointestinal tract.

In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observations in many domains. Further, in many cases the target entities for analysis are actually signals on such graphs. We propose to compare and organize such datasets of graph signals by using an earth mover’s distance (EMD) with a geodesic cost over the underlying graph. Typically, EMD is computed by optimizing over the cost of transporting one probability distribution to another over an underlying metric space. However, this is inefficient when computing the EMD between many signals. Here, we propose an unbalanced graph EMD that efficiently embeds the unbalanced EMD on an underlying graph into an L(1) space, whose metric we call unbalanced diffusion earth mover’s distance (UDEMD). Next, we show how this gives distances between graph signals that are robust to noise. Finally, we apply this to organizing patients based on clinical notes, embedding cells modeled as signals on a gene graph, and organizing genes modeled as signals over a large cell graph. In each case, we show that UDEMD-based embeddings find accurate distances that are highly efficient compared to other methods.

BACKGROUND The number of artificial intelligence (AI) studies in medicine has exponentially increased recently. However, there is no clear quantification of clinical benefit when AI-assisted tools are implemented in patient care. OBJECTIVE We aim to systematically review all published randomized controlled trials (RCTs) of AI-assisted tools to characterize their performance in clinical practice. METHODS CINAHL, Cochrane Central, Embase, Medline and PubMed were searched to identify relevant RCTs comparing the performance of AI-assisted tool against conventional clinical management without AI-assistance published up to July 2021. We evaluated the primary endpoints of each study to determine which were clinically relevant. RESULTS Among 11,839 articles searched, only 38 RCTs identified were included. These RCTs were conducted in a roughly equal distribution from North America, Europe, and Asia. AI-assisted tools were implemented in 13 different clinical specialties. Most RCTs were published in the field of Gastroenterology, with 15 studies on AI-assisted endoscopy. The majority of RCTs studied image-based AI-assisted tools, and a minority of RCTs studied AI-assisted tools that drew from tabular patient. In 29 out of 38 RCTs, AI-assisted interventions outperformed usual clinical care, and clinically relevant outcomes improved with AI assisted intervention in 21 out of 29 studies. Small sample size and single-centre design limit the generalizability of these studies. CONCLUSIONS There is growing evidence supporting the implementation of AI-assisted tool in daily clinical practice, yet the number of available RCTs is limited and heterogeneous. Future studies are needed to quantify the benefit of AI-assisted tools in clinical practice. CLINICALTRIAL This study was registered on PROSPERO (ID: CRD42021286539).

Risk stratification of patients with gastrointestinal bleeding (GIB) is recommended, but current risk assessment tools have variable performance. Machine learning (ML) has promise to improve risk assessment. We performed a systematic review to evaluate studies utilizing ML techniques for GIB. Bibliographic databases and conference abstracts were searched for studies with a population of overt GIB that used an ML algorithm with outcomes of mortality, rebleeding, hemostatic intervention, and/or hospital stay. Two independent reviewers screened titles and abstracts, reviewed full-text studies, and extracted data from included studies. Risk of bias was assessed with an adapted Quality in Prognosis Studies tool. Area under receiver operating characteristic curves (AUCs) were the primary assessment of performance with AUC ≥ 0.80 predefined as an acceptable threshold of good performance. Fourteen studies with 30 assessments of ML models met inclusion criteria. No study had low risk of bias. Median AUC reported in validation datasets for predefined outcomes of mortality, intervention, or rebleeding was 0.84 (range 0.40-0.98). AUCs were higher with artificial neural networks (median 0.93, range 0.78-0.98) than other ML models (0.81, range 0.40-0.92). ML performed better than clinical risk scores (Glasgow-Blatchford, Rockall, Child-Pugh, MELD) for mortality in upper GIB. Limitations include heterogeneity of ML models, inconsistent comparisons of ML models with clinical risk scores, and high risk of bias. ML generally provided good-excellent prognostic performance in patients with GIB, and artificial neural networks tended to outperform other ML models. ML was better than clinical risk scores for mortality in upper GIB.

Machine learning, a subset of artificial intelligence (AI), is a set of computational tools that can be used to enhance provision of clinical care in all areas of medicine. Gastroenterology and hepatology utilize multiple sources of information, including visual findings on endoscopy, radiologic imaging, manometric testing, genomes, proteomes, and metabolomes. However, clinical care is complex and requires a thoughtful approach to best deploy AI tools to improve quality of care and bring value to patients and providers. On the operational level, AI-assisted clinical management should consider logistic challenges in care delivery, data management, and algorithmic stewardship. There is still much work to be done on a broader societal level in developing ethical, regulatory, and reimbursement frameworks. A multidisciplinary approach and awareness of AI tools will create a vibrant ecosystem for using AI-assisted tools to guide and enhance clinical practice. From optically enhanced endoscopy to clinical decision support for risk stratification, AI tools will potentially transform our practice by leveraging massive amounts of data to personalize care to the right patient, in the right amount, at the right time.

Summary Often when biological entities are measured in multiple ways, there are distinct categories of information: some information is easy-to-obtain information (EI) and can be gathered on virtually every subject of interest, while other information is hard-to-obtain information (HI) and can only be gathered on some. We propose building a model to make probabilistic predictions of HI using EI. Our feature mapping GAN (FMGAN), based on the conditional GAN framework, uses an embedding network to process conditions as part of the conditional GAN training to create manifold structure when it is not readily present in the conditions. We experiment on generating RNA sequencing of cell lines perturbed with a drug conditioned on the drug's chemical structure and generating FACS data from clinical monitoring variables on a cohort of COVID-19 patients, effectively describing their immune response in great detail., Highlights • Modeling RNA sequencing of cells perturbed with a drug with its chemical structure • Modeling patients' FACS from clinical monitoring data • Enabling cGANs to learn their own condition space, The bigger picture Many experiments face a trade-off between gathering easy-to-collect information on many samples or hard-to-collect information on a smaller number of samples due to costs in terms of both money and time. We demonstrate that a mapping between the easy-to-collect and hard-to collect information can be trained as a conditional GAN from a subset of samples with both measured. With our conditional GAN model known as feature mapping GAN (FMGAN), the results of expensive experiments can be predicted, saving on the costs of actually performing the experiment. We study two example settings where this could have impact: pharmaceutical drug discovery, where early phase experiments require casting a wide net to find just a few potential leads to follow. FMGAN can also have a major impact in clinical setting, where standard measurements early in a stay can predict values of later single-cell-resolution samples., Many experiments face a trade-off between gathering easy-to-collect information on many samples or hard-to-collect information on a smaller number of samples due to costs in terms of both money and time. With the model introduced here called the FMGAN, the results of expensive experiments can be predicted, saving on the costs of actually performing the experiment.

Background and AimGuidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time within electronic health records (EHRs) would overcome a major impediment. This requires an automated mechanism to accurately identify (“phenotype”) patients with GIB at the time of presentation. The goal is to identify patients with acute GIB by developing and evaluating EHR-based phenotyping algorithms for emergency department (ED) patients.MethodsWe specified criteria using structured data elements to create rules for identifying patients, and also developed a natural-language-processing (NLP)-based algorithm for automated phenotyping of patients, tested them with tenfold cross-validation (n=7144) and external validation (n=2988), and compared them with the standard method for encoding patient conditions in the EHR, Systematized Nomenclature of Medicine (SNOMED). The gold standard for GIB diagnosis was independent dual manual review of medical records. The primary outcome was positive predictive value (PPV).ResultsA decision rule using GIB-specific terms from ED triage and from ED review-of-systems assessment performed better than SNOMED on internal validation (PPV=91% [90%-93%] vs. 74% [71%-76%], PPConclusionsAn automated decision rule employing GIB-specific triage and review-of-systems terms can be used to trigger EHR-based deployment of risk stratification models to guide clinical decision-making in real time for patients with acute GIB presenting to the ED.

Structured SummaryBackgroundAcute gastrointestinal bleeding is the most common gastrointestinal cause for hospitalization. For high risk patients requiring intensive care unit stay, predicting transfusion needs during the first 24 hours using dynamic risk assessment may improve resuscitation.AimsProvide dynamic risk prediction for red blood cell transfusion in admitted patients with severe acute gastrointestinal bleeding.MethodsA patient cohort admitted for acute gastrointestinal bleeding (N = 2,524) was identified from the Medical Information Mart for Intensive Care III (MIMIC-III) critical care database, separated into training (N = 2,032) and validation (N = 492) sets. 74 demographic, clinical, and laboratory test features were consolidated into 4-hour time intervals over the first 24 hours from admission. The outcome measure was the transfusion of red blood cells during each 4-hour time interval. A long short-term memory (LSTM) model, a type of Recurrent Neural Network (RNN), was compared to the Glasgow-Blatchford Score (GBS).ResultsThe LSTM model performed better than GBS overall (AUROC 0.81 vs 0.63;P)and at each 4-hour interval (PPPPPPConclusionsA LSTM model can be used to predict the need for transfusion of packed red blood cells over the first 24 hours from admission to help personalize the care of high-risk patients with acute gastrointestinal bleeding.Data AccessAll clinical data from MIMIC-III was approved under the oversight of the Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA). Requirement for individual patient consent was waived because the project did not impact clinical care and all protected health information was deidentified. The data was available on PhysioNet were derived from protected health information that has been de-identified and not subject to HIPAA Privacy Rule restrictions. All use of the data was performed with credentialed access under the oversight of the data use agreement through PhysioNet and the Massachusetts Institute of Technology.

Risk assessment tools for patients with gastrointestinal bleeding may be used for determining level of care and informing management decisions. Development of models that use data from electronic health records is an important step for future deployment of such tools in clinical practice. Furthermore, machine learning tools have the potential to outperform standard clinical risk assessment tools. The authors developed a new machine learning tool for the outcome of in-hospital mortality and suggested it outperforms the intensive care unit prognostic tool, APACHE IVa. Limitations include lack of generalizability beyond intensive care unit patients, inability to use early in the hospital course, and lack of external validation.

Discovering patterns of the complex high-dimensional data is a long-standing problem. Dimension Reduction (DR) and Intrinsic Dimension Estimation (IDE) are two fundamental thematic programs that facilitate geometric understanding of the data. We present Rdimtools - an R package that supports 133 DR and 17 IDE algorithms whose extent makes multifaceted scrutiny of the data in one place easier. Rdimtools is distributed under the MIT license and is accessible from CRAN, GitHub, and its package website, all of which deliver instruction for installation, self-contained examples, and API documentation.

The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. Here we present Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Our approach creates a tree of data granularities that can be cut at coarse levels for high level summarizations, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Our analysis identifies pathogenic cellular populations, CD16-hiCD66b-lo neutrophils and IFNγ+GranzymeB+ Th17 cells, and shows that cellular groupings discovered by Multiscale PHATE are directly predictive of disease outcome. We use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another on patient clinical features, both associating immune subsets and clinical markers with outcome. Conflict of Interest: Dr. Krishnaswamy is on the scientific advisory board of KovaDx and AI Therapeutics. Dr. Iwasaki a member of the SAB for InProTher. Dr. Iwasaki is a co-founder of RIGImmune. Dr. Wilson is founder of Efference. Dr. Ko is a member of the expert panel of the Reckit Global Hygiene Institute. The remaining authors have no competing interests to declare. Ethical Approval: This study was approved by Yale Human Research Protection Program Institutional Review Boards (FWA00002571, protocol ID 2000027690). Informed consent was obtained from all enrolled patients and healthcare workers.

Background & Aims: Scoring systems are suboptimal for determining risk in patients with upper gastrointestinal bleeding (UGIB); these might be improved by a machine learning model. We used machine learning to develop a model to calculate the risk of hospital-based intervention or death in patients with UGIB and compared its performance with other scoring systems. Methods: We analyzed data collected from consecutive unselected patients with UGIB from medical centers in 4 countries (the United States, Scotland, England, and Denmark; n = 1958) from March 2014 through March 2015. We used the data to derive and internally validate a gradient-boosting machine learning model to identify patients who met a composite endpoint of hospital-based intervention (transfusion or hemostatic intervention) or death within 30 days. We compared the performance of the machine learning prediction model with validated pre-endoscopic clinical risk scoring systems (the Glasgow-Blatchford score [GBS], admission Rockall score, and AIMS65). We externally validated the machine learning model using data from 2 Asia-Pacific sites (Singapore and New Zealand; n = 399). Performance was measured by area under receiver operating characteristic curve (AUC) analysis. Results: The machine learning model identified patients who met the composite endpoint with an AUC of 0.91 in the internal validation set; the clinical scoring systems identified patients who met the composite endpoint with AUC values of 0.88 for the GBS (P = .001), 0.73 for Rockall score (P < .001), and 0.78 for AIMS65 score (P < .001). In the external validation cohort, the machine learning model identified patients who met the composite endpoint with an AUC of 0.90, the GBS with an AUC of 0.87 (P = .004), the Rockall score with an AUC of 0.66 (P < .001), and the AIMS65 with an AUC of 0.64 (P < .001). At cutoff scores at which the machine learning model and GBS identified patients who met the composite endpoint with 100% sensitivity, the specificity values were 26% with the machine learning model versus 12% with GBS (P < .001). Conclusions: We developed a machine learning model that identifies patients with UGIB who met a composite endpoint of hospital-based intervention or death within 30 days with a greater AUC and higher levels of specificity, at 100% sensitivity, than validated clinical risk scoring systems. This model could increase identification of low-risk patients who can be safely discharged from the emergency department for outpatient management.

Background & Aims Guidelines recommend colonoscopy evaluation within 24 hours of presentation or admission in patients with high-risk or severe acute lower gastrointestinal bleeding (LGIB). Meta-analyses of the timing of colonoscopy have relied primarily on observational studies that had major potential for bias. We performed a systematic review of randomized trials to determine optimal timing of colonoscopy for patients hospitalized with acute LGIB. Methods We searched publication databases through July 2019 and abstracts from gastroenterology meetings through November 2019 for randomized trials of patients with acute LGIB or hematochezia. We searched for studies that compared early colonoscopy (within 24 hours) with elective colonoscopy beyond 24 hours and/or other diagnostic tests. Our primary outcome was further bleeding, defined as persistent or recurrent bleeding after index examination. Secondary outcomes included mortality, diagnostic yield (identifying source of bleeding), endoscopic intervention, and any primary hemostatic intervention (endoscopic, surgical, or interventional radiologic). We performed dual independent review, data extraction, and risk of bias assessments. We performed the meta-analysis using a random-effects model. Results Our final analysis included data from 4 randomized trials. Further bleeding was not decreased among patients who received early vs later, elective colonoscopy (relative risk [RR] for further bleeding with early colonoscopy, 1.57; 95% CI. 0.74–3.31). We did not find significant differences in the secondary outcomes of mortality (RR, 0.93; 95% CI, 0.05–17.21), diagnostic yield (RR, 1.09; 95% CI, 0.99–1.21), endoscopic intervention (RR, 1.53; 95% CI, 0.67–3.48), or any primary hemostatic intervention (RR, 1.33; 95% CI, 0.92–1.92). Conclusions In a meta-analysis of randomized trials, we found that colonoscopy within 24 hours does not reduce further bleeding or mortality in patients hospitalized with acute LGIB. Based on these findings, patients hospitalized with acute LGIB do not generally require early colonoscopy.

BACKGROUND & AIMS: Improved methods are needed to risk stratify and predict outcomes in patients with primary sclerosing cholangitis (PSC). Therefore, we sought to derive and validate a new prediction model and compare its performance to existing surrogate markers. METHODS: The model was derived using 509 subjects from a multicenter North American cohort and validated in an international multicenter cohort (n=278). Gradient boosting, a machine based learning technique, was used to create the model. The endpoint was hepatic decompensation (ascites, variceal hemorrhage or encephalopathy). Subjects with advanced PSC or cholangiocarcinoma at baseline were excluded. RESULTS: The PSC risk estimate tool (PREsTo) consists of 9 variables: bilirubin, albumin, serum alkaline phosphatase (SAP) times the upper limit of normal (ULN), platelets, AST, hemoglobin, sodium, patient age and the number of years since PSC was diagnosed. Validation in an independent cohort confirms PREsTo accurately predicts decompensation (C statistic 0.90, 95% confidence interval (CI) 0.84-0.95) and performed well compared to MELD score (C statistic 0.72, 95% CI 0.57-0.84), Mayo PSC risk score (C statistic 0.85, 95% CI 0.77-0.92) and SAP < 1.5x ULN (C statistic 0.65, 95% CI 0.55-0.73). PREsTo continued to be accurate among individuals with a bilirubin < 2.0 mg/dL (C statistic 0.90, 95% CI 0.82-0.96) and when the score was re-applied at a later course in the disease (C statistic 0.82, 95% CI 0.64-0.95). CONCLUSIONS: PREsTo accurately predicts hepatic decompensation in PSC and exceeds the performance among other widely available, noninvasive prognostic scoring systems.

INTRODUCTION: The hospitalist field has grown exponentially in internal medicine, pediatrics, and other specialties over the last 20 years GI hospitalists have existed for years, but little is known about their impact on productivity We aim to determine the effect of introducing a GI hospitalist model on endoscopic procedure volume Our hypothesis was that inpatient endoscopic volume would increase and outpatient volume would rise given reduced inpatient responsibilities for other attending physicians METHODS: A two-attending GI hospitalist model was introduced at a large academic center 7/2019 GI hospitalists did not perform outpatient procedures An interrupted time series design was deployed: pre-intervention time period was 9/1/2018 to 3/1/2019, matched to post-intervention time period 9/1/2019 to 3/1/2020 with a 2-month run-in time from 7/1/2019 to 9/1/2019 to allow for transition to the new model Segmented regression was used to compare total procedure volume, both inpatient and outpatient, at the institution's 4 endoscopy units Assessment was stopped 3/1/2020 to reduce influence from COVID-19 Primary outcome was total endoscopic procedures RESULTS: A total of 29 providers were included Other than addition of hospitalists, the number of endoscopy providers did not change from the pre-intervention to post-intervention period Total endoscopic procedures increased 1077/5444 ((20%) P = 0 01);with similar increases in inpatient procedures (328/1472 (22%), P5, 0 001) and outpatient procedures (752/3969 (19%), P = 0 02) (Table 1) The pre- and post-implementation groups were similar in age and sex (Table 1) CONCLUSION: Introduction of a GI hospitalist model increased overall endoscopy volume, with similar increases for inpatient and outpatient procedures When gastroenterologists cover both inpatient and outpatient responsibilities, productivity may decrease due to need to cancel outpatient procedures while on inpatient service and reduced availability for urgent inpatient endoscopies related to outpatient responsibilities A GI hospitalist model reduces these disruptions by using inhospital providers to cover unpredictable inpatient needs, allowing outpatient providers to continue scheduled procedures without interruptions Implementing a GI hospitalist model resulted in increased procedural volume However, further study is needed to evaluate the duration of this effect and to evaluate the effect on clinic productivity and quality metrics

Drug-induced liver injury (DILI) accounts for about 50% of acute liver failure cases in the United States. Diagnosis is challenging, especially due to the myriad combinations of potentially hepatotoxic medications and clinical presentations. Unexplained liver injury should prompt a thorough investigation of medication administration (e.g., for accidental or intentional overdose) and the use of herbal and dietary supplements. The framework for approaching DILI includes the following: (1) categorize the injury as either intrinsic or idiosyncratic, (2) establish time course and pattern of injury, and (3) triage effectively to minimize mortality risk.

The complications of therapy, hospitalization, and surgery related to inflammatory bowel disease (IBD) in the elderly are not well described. While multiple reviews have described the management and complications of elderly patients with IBD, none have been performed in a systematic fashion. We performed a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to evaluate the association between elderly patients with IBD and complications from therapy, hospitalizations, and surgery. Eligible studies were identified via structured keyword searches in PubMed and manual literature searches. A total of 5,644 publications were identified. Of these, fourteen studies met inclusion criteria, encompassing 963 elderly IBD patients (113 Crohn’s disease and 850 ulcerative colitis patients), over 37,000 hospitalizations of elderly IBD patients and over 4,500 controls. Consistent associations were observed between increased age and higher nocturnal stool frequency post-ileal pouch anal anastomosis. Only two studies met inclusion criteria for medication-related complications, one observed an increased mortality and infection risk among elderly patients treated with tumor necrosis factor antagonists and the other observed increased hospital-related complications among elderly patients treated with steroids. Elderly patients with IBD are at an increased risk of hospital- and therapy-related complications. We found a paucity of high-quality studies evaluating outcomes in elderly patients with IBD. Further studies of elderly patients with IBD are needed to further evaluate the effect of age on medical and surgical complications.