Preoperative risk evaluation and optimization for patients with liver disease.

The prevalence of liver disease is rising and more patients with liver disease are considered for surgery each year. Liver disease poses many potential complications to surgery; therefore, assessing perioperative risk and optimizing a patient's liver health is necessary to decrease perioperative risk. Multiple scoring tools exist to help quantify perioperative risk and can be used in combination to best educate patients prior to surgery. In this review, we go over the various scoring tools and provide a guide for clinicians to best assess and optimize perioperative risk based on the etiology of liver disease.

Performance of International Classification of Disease-10 codes in detecting emergency department patients with opioid misuse.

BACKGROUND AND AIMS: Accurate case discovery is critical for disease surveillance, resource allocation and research. International Classification of Disease (ICD) diagnosis codes are commonly used for this purpose. We aimed to determine the sensitivity, specificity and positive predictive value (PPV) of ICD-10 codes for opioid misuse case discovery in the emergency department (ED) setting. DESIGN AND SETTING: Retrospective cohort study of ED encounters from January 2018 to December 2020 at an urban academic hospital in the United States. A sample of ED encounters enriched for opioid misuse was developed by oversampling ED encounters with positive urine opiate screens or pre-existing opioid-related diagnosis codes in addition to other opioid misuse risk factors. CASES: A total of 1200 randomly selected encounters were annotated by research staff for the presence of opioid misuse within health record documentation using a 5-point scale for likelihood of opioid misuse and dichotomized into cohorts of opioid misuse and no opioid misuse. MEASUREMENTS: Using manual annotation as ground truth, the sensitivity and specificity of ICD-10 codes entered during the encounter were determined with PPV adjusted for oversampled data. Metrics were also determined by disposition subgroup: discharged home or admitted. FINDINGS: There were 541 encounters annotated as opioid misuse and 617 with no opioid misuse. The majority were males (54.4%), average age was 47 years and 68.5% were discharged directly from the ED. The sensitivity of ICD-10 codes was 0.56 (95% confidence interval [CI], 0.51-0.60), specificity 0.99 (95% CI, 0.97-0.99) and adjusted PPV 0.78 (95% CI, 0.65-0.92). The sensitivity was higher for patients discharged from the ED (0.65; 95% CI, 0.60-0.69) than those admitted (0.31; 95% CI, 0.24-0.39). CONCLUSIONS: International Classification of Disease-10 codes appear to have low sensitivity but high specificity and positive predictive value in detecting opioid misuse among emergency department patients in the United States.

Machine Learning Techniques to Explore Clinical Presentations of COVID-19 Severity and to Test the Association With Unhealthy Opioid Use: Retrospective Cross-sectional Cohort Study.

BACKGROUND: The COVID-19 pandemic has exacerbated health inequities in the United States. People with unhealthy opioid use (UOU) may face disproportionate challenges with COVID-19 precautions, and the pandemic has disrupted access to opioids and UOU treatments. UOU impairs the immunological, cardiovascular, pulmonary, renal, and neurological systems and may increase severity of outcomes for COVID-19. OBJECTIVE: We applied machine learning techniques to explore clinical presentations of hospitalized patients with UOU and COVID-19 and to test the association between UOU and COVID-19 disease severity. METHODS: This retrospective, cross-sectional cohort study was conducted based on data from 4110 electronic health record patient encounters at an academic health center in Chicago between January 1, 2020, and December 31, 2020. The inclusion criterion was an unplanned admission of a patient aged ≥18 years; encounters were counted as COVID-19-positive if there was a positive test for COVID-19 or 2 COVID-19 International Classification of Disease, Tenth Revision codes. Using a predefined cutoff with optimal sensitivity and specificity to identify UOU, we ran a machine learning UOU classifier on the data for patients with COVID-19 to estimate the subcohort of patients with UOU. Topic modeling was used to explore and compare the clinical presentations documented for 2 subgroups: encounters with UOU and COVID-19 and those with no UOU and COVID-19. Mixed effects logistic regression accounted for multiple encounters for some patients and tested the association between UOU and COVID-19 outcome severity. Severity was measured with 3 utilization metrics: low-severity unplanned admission, medium-severity unplanned admission and receiving mechanical ventilation, and high-severity unplanned admission with in-hospital death. All models controlled for age, sex, race/ethnicity, insurance status, and BMI. RESULTS: Topic modeling yielded 10 topics per subgroup and highlighted unique comorbidities associated with UOU and COVID-19 (eg, HIV) and no UOU and COVID-19 (eg, diabetes). In the regression analysis, each incremental increase in the classifier's predicted probability of UOU was associated with 1.16 higher odds of COVID-19 outcome severity (odds ratio 1.16, 95% CI 1.04-1.29; P=.009). CONCLUSIONS: Among patients hospitalized with COVID-19, UOU is an independent risk factor associated with greater outcome severity, including in-hospital death. Social determinants of health and opioid-related overdose are unique comorbidities in the clinical presentation of the UOU patient subgroup. Additional research is needed on the role of COVID-19 therapeutics and inpatient management of acute COVID-19 pneumonia for patients with UOU. Further research is needed to test associations between expanded evidence-based harm reduction strategies for UOU and vaccination rates, hospitalizations, and risks for overdose and death among people with UOU and COVID-19. Machine learning techniques may offer more exhaustive means for cohort discovery and a novel mixed methods approach to population health.

Association of Pancreatic Fatty Infiltration With Age and Metabolic Syndrome Is Sex-Dependent.

Background and Aims: Fatty infiltration of the pancreas has been shown to be associated with both precancerous pancreatic lesions and pancreatic ductal adenocarcinoma. We aim to determine predictors of fatty infiltration of the pancreas in United States adults. Methods: In this retrospective cohort study conducted at a large academic hospital in Chicago, Illinois, we calculated the relative fatty infiltration of the pancreas (corrected to spleen) of 265 cancer-free individuals based on their cross-sectional imaging. Demographic data and relevant laboratory results were obtained from medical records. Results: We found that age was the strongest predictor of fatty infiltration of the pancreas in our series (P < .01). Fatty infiltration of the pancreas was also significantly associated with body mass index (P < .01) and hyperlipidemia (P < .05). In women, in addition to age (P < .05), elevated body mass index (P = .023), hyperlipidemia (P = .013), and fatty liver (P = .017) were predictors of fat in pancreas. We found a sex-dependent association between pancreatic fat and metabolic syndrome including fatty liver (P = .002). Conclusion: Fatty infiltration of the pancreas increases by age and components of metabolic syndrome. These assertions could be sex-dependent.

Investigating Unhealthy Alcohol Use As an Independent Risk Factor for Increased COVID-19 Disease Severity: Observational Cross-sectional Study.

BACKGROUND: Unhealthy alcohol use (UAU) is known to disrupt pulmonary immune mechanisms and increase the risk of acute respiratory distress syndrome in patients with pneumonia; however, little is known about the effects of UAU on outcomes in patients with COVID-19 pneumonia. To our knowledge, this is the first observational cross-sectional study that aims to understand the effect of UAU on the severity of COVID-19. OBJECTIVE: We aim to determine if UAU is associated with more severe clinical presentation and worse health outcomes related to COVID-19 and if socioeconomic status, smoking, age, BMI, race/ethnicity, and pattern of alcohol use modify the risk. METHODS: In this observational cross-sectional study that took place between January 1, 2020, and December 31, 2020, we ran a digital machine learning classifier on the electronic health record of patients who tested positive for SARS-CoV-2 via nasopharyngeal swab or had two COVID-19 International Classification of Disease, 10th Revision (ICD-10) codes to identify patients with UAU. After controlling for age, sex, ethnicity, BMI, smoking status, insurance status, and presence of ICD-10 codes for cancer, cardiovascular disease, and diabetes, we then performed a multivariable regression to examine the relationship between UAU and COVID-19 severity as measured by hospital care level (ie, emergency department admission, emergency department admission with ventilator, or death). We used a predefined cutoff with optimal sensitivity and specificity on the digital classifier to compare disease severity in patients with and without UAU. Models were adjusted for age, sex, race/ethnicity, BMI, smoking status, and insurance status. RESULTS: Each incremental increase in the predicted probability from the digital alcohol classifier was associated with a greater odds risk for more severe COVID-19 disease (odds ratio 1.15, 95% CI 1.10-1.20). We found that patients in the unhealthy alcohol group had a greater odds risk to develop more severe disease (odds ratio 1.89, 95% CI 1.17-3.06), suggesting that UAU was associated with an 89% increase in the odds of being in a higher severity category. CONCLUSIONS: In patients infected with SARS-CoV-2, UAU is an independent risk factor associated with greater disease severity and/or death.

Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups.

OBJECTIVES: To assess fairness and bias of a previously validated machine learning opioid misuse classifier. MATERIALS & METHODS: Two experiments were conducted with the classifier's original (n = 1000) and external validation (n = 53 974) datasets from 2 health systems. Bias was assessed via testing for differences in type II error rates across racial/ethnic subgroups (Black, Hispanic/Latinx, White, Other) using bootstrapped 95% confidence intervals. A local surrogate model was estimated to interpret the classifier's predictions by race and averaged globally from the datasets. Subgroup analyses and post-hoc recalibrations were conducted to attempt to mitigate biased metrics. RESULTS: We identified bias in the false negative rate (FNR = 0.32) of the Black subgroup compared to the FNR (0.17) of the White subgroup. Top features included "heroin" and "substance abuse" across subgroups. Post-hoc recalibrations eliminated bias in FNR with minimal changes in other subgroup error metrics. The Black FNR subgroup had higher risk scores for readmission and mortality than the White FNR subgroup, and a higher mortality risk score than the Black true positive subgroup (P < .05). DISCUSSION: The Black FNR subgroup had the greatest severity of disease and risk for poor outcomes. Similar features were present between subgroups for predicting opioid misuse, but inequities were present. Post-hoc mitigation techniques mitigated bias in type II error rate without creating substantial type I error rates. From model design through deployment, bias and data disadvantages should be systematically addressed. CONCLUSION: Standardized, transparent bias assessments are needed to improve trustworthiness in clinical machine learning models.

External validation of an opioid misuse machine learning classifier in hospitalized adult patients.

BACKGROUND: Opioid misuse screening in hospitals is resource-intensive and rarely done. Many hospitalized patients are never offered opioid treatment. An automated approach leveraging routinely captured electronic health record (EHR) data may be easier for hospitals to institute. We previously derived and internally validated an opioid classifier in a separate hospital setting. The aim is to externally validate our previously published and open-source machine-learning classifier at a different hospital for identifying cases of opioid misuse. METHODS: An observational cohort of 56,227 adult hospitalizations was examined between October 2017 and December 2019 during a hospital-wide substance use screening program with manual screening. Manually completed Drug Abuse Screening Test served as the reference standard to validate a convolutional neural network (CNN) classifier with coded word embedding features from the clinical notes of the EHR. The opioid classifier utilized all notes in the EHR and sensitivity analysis was also performed on the first 24 h of notes. Calibration was performed to account for the lower prevalence than in the original cohort. RESULTS: Manual screening for substance misuse was completed in 67.8% (n = 56,227) with 1.1% (n = 628) identified with opioid misuse. The data for external validation included 2,482,900 notes with 67,969 unique clinical concept features. The opioid classifier had an AUC of 0.99 (95% CI 0.99-0.99) across the encounter and 0.98 (95% CI 0.98-0.99) using only the first 24 h of notes. In the calibrated classifier, the sensitivity and positive predictive value were 0.81 (95% CI 0.77-0.84) and 0.72 (95% CI 0.68-0.75). For the first 24 h, they were 0.75 (95% CI 0.71-0.78) and 0.61 (95% CI 0.57-0.64). CONCLUSIONS: Our opioid misuse classifier had good discrimination during external validation. Our model may provide a comprehensive and automated approach to opioid misuse identification that augments current workflows and overcomes manual screening barriers.

Description and analysis of patients and outcomes following third-time heart transplantation: An analysis of the United Network for Organ Sharing database from 1985 to 2017.

BACKGROUND: Following second heart transplantation (HTx), some patients experience graft failure and require third-time heart transplantation. Little data exist to guide decision-making with regard to repeat retransplantation in older patients. METHODS: We performed a retrospective cohort analysis of patients receiving a third HTx, as identified in the United Network for Organ Sharing (UNOS) database from 1985 to 2017. RESULTS: The study cohort consisted of N = 60 patients, with an average age of 29 with a standard deviation of ±18 years. Overall survival for the cohort at 1, 5, and 10 years is 83%, 64%, and 44%, respectively. The rate of third-time HTxs has steadily increased in all age groups. Patients older than 50 years now account for 18.3% of all third-time HTxs. Although this group demonstrated longer average previous graft survival, after third HTx they demonstrate significantly poorer survival outcomes compared to third-time HTx recipients younger than 21 (P = 0.05). Age over 50, BMI over 30, and diabetes were all found to be independent risk factors for decreased survival following third HTx. CONCLUSIONS: We describe trends in patients undergoing third HTx. We highlight subsets of such recipients who exhibit decreased survival.

Postnatal Development of the Murine Notochord Remnants Quantified by High-resolution Contrast-enhanced MicroCT.

The notochord gives rise to spinal segments during development, and it becomes embedded within the nucleus pulposus of the intervertebral disc (IVD) during maturation. The disruption of the notochord band has been observed with IVD degeneration. Since the mechanical competence of the IVD relies on its structural constituents, defining the structure of the notochord during aging is critical for investigations relating to IVD function and homeostasis. The assessment and imaging of the notochord has classically relied on histological techniques, which introduces sectioning artifacts during preparation and spatial biases. Magnetic resonance imaging (MRI) does not offer sufficient resolution to discriminate the notochord from the surrounding the nucleus pulposus, especially in murine models. Current X-ray based computed tomography systems provide imaging resolutions down to the single- and sub- micron scales, and when coupled with contrast-enhancing agents, enable the high-resolution three-dimensional imaging of relatively small features. Utilizing phosphomolybdic acid to preferentially bind to collagen cationic domains, we describe the structure of the notochord remnants with aging in the lumbar IVDs of BALB/c mice. These results provide a highly quantitative and sensitive approach to monitoring the IVD during postnatal development.

An In Vitro Organ Culture Model of the Murine Intervertebral Disc.

Intervertebral disc (IVD) degeneration is a significant contributor to low back pain. The IVD is a fibrocartilaginous joint that serves to transmit and dampen loads in the spine. The IVD consists of a proteoglycan-rich nucleus pulposus (NP) and a collagen-rich annulus fibrosis (AF) sandwiched by cartilaginous end-plates. Together with the adjacent vertebrae, the vertebrae-IVD structure forms a functional spine unit (FSU). These microstructures contain unique cell types as well as unique extracellular matrices. Whole organ culture of the FSU preserves the native extracellular matrix, cell differentiation phenotypes, and cellular-matrix interactions. Thus, organ culture techniques are particularly useful for investigating the complex biological mechanisms of the IVD. Here, we describe a high-throughput approach for culturing whole lumbar mouse FSUs that provides an ideal platform for studying disease mechanisms and therapies for the IVD. Furthermore, we describe several applications that utilize this organ culture method to conduct further studies including contrast-enhanced microCT imaging and three-dimensional high-resolution finite element modeling of the IVD.