Use of inflammatory markers as predictor for mechanical ventilation in COVID-19 patients with stages IIIb-V chronic kidney disease?

BACKGROUND

Studies have shown elevated C-reactive protein (CRP) to predict mechanical ventilation (MV) in patients with coronavirus disease 2019 (COVID-19). Its utility is unknown in patients with chronic kidney disease (CKD), who have elevated baseline CRP levels due to chronic inflammation and reduced renal clearance.

AIM

To assess whether an association exists between elevated inflammatory markers and MV rate in patients with stages IIIb-V CKD and COVID-19.

METHODS

We conducted a retrospective cohort study on patients with COVID-19 and stages IIIb-V CKD. The primary outcome was the rate of invasive MV, the rate of noninvasive MV, and the rate of no MV. Statistical analyses used unpaired t-test for continuous variables and chi-square analysis for categorical variables. Cutoffs for variables were CRP: 100 mg/L, ferritin: 530 ng/mL, D-dimer: 0.5 mg/L, and lactate dehydrogenase (LDH): 590 U/L.

RESULTS

290 were screened, and 118 met the inclusion criteria. CRP, D-dimer, and ferritin were significantly different among the three groups. On univariate analysis for invasive MV (IMV), CRP had an odds ratio (OR)-5.44; ferritin, OR-2.8; LDH, OR-7.7; D-dimer, OR-3.9, (P < 0.05). The admission CRP level had an area under curve-receiver operator characteristic (AUROC): 0.747 for the IMV group (sensitivity-80.8%, specificity-50%) and 0.663 for the non-IMV (NIMV) group (area under the curve, sensitivity-69.2%, specificity-53%).

CONCLUSION

Our results demonstrate a positive correlation between CRP, ferritin, and D-dimer levels and MV and NIMV rates in CKD patients. The AUROC demonstrates a good sensitivity for CRP levels in detecting the need for MV in patients with stages IIIb-V CKD. This may be because of the greater magnitude of increased inflammation due to COVID-19 itself compared with increased inflammation and reduced clearance due to CKD alone.

Case-control analysis of venous thromboembolism risk in non-alcoholic steatohepatitis diagnosed by transient elastography

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide. Studies have shown a strong association between non-alcoholic steatohepatitis (NASH) cirrhosis and portal vein thrombosis. Specifically, there is paucity of data on the association of NASH and venous thromboembolism (VTE), with one such study predicting a 2.5-fold increased risk for VTE compared to other liver diseases in hospitalized patients. The mechanism is believed to be a hepatocellular injury, which causes a chronic inflammatory state leading to the unregulated activation of procoagulant factors. There has been no prior analysis of the degree of steatosis and fibrosis (measured using transient elastography, commonly known as FibroScan) in NASH and its association with VTE.

AIM

To examine the association between the degree of hepatic steatosis and fibrosis, quantified by transient elastography, and the incidence of VTE in patients with NASH.

METHODS

In our case-control study, we included patients with a documented diagnosis of NASH. We excluded patients with inherited thrombophilia, hemoglobinopathy, malignancy, alcohol use disorder, autoimmune hepatitis, and primary biliary cirrhosis. The collected data included age, demographics, tobacco use, recreational drug use, medical history, and vibration controlled transient elastography scores. VTE-specific data included the location, type of anticoagulant, need for hospital stay, and history of VTE recurrence. Steatosis was categorized as S0-S1 (mild) and S2-S3 (moderate to severe) based on the controlled attenuation parameter score. Fibrosis was classified based on the kilopascal score and graded as F0-F1 (Metavir stage), F2, F3, and F4 (cirrhosis). χ2 and Mann-Whitney U tests were used for the qualitative and quantitative variable analyses, respectively. Furthermore, we performed a logistic regression using VTE as the dependent variable.

RESULTS

A total of 415 patients were analyzed, and 386 met the inclusion criteria. 51 and 335 patients were included in the VTE and non-VTE groups, respectively. Patients with VTE had a mean age of 60.63 years compared to 55.22 years in the non-VTE group (P < 0.014). Patients with VTE had a higher body mass index (31.14 kg/m² vs 29.30 kg/m²) and a higher prevalence of diabetes mellitus (29.4% vs 13.1%). The history of NASH was significantly higher in the VTE group (45.1% vs 30.4%, P < 0.037). Furthermore, moderate-to-severe steatosis was significantly higher in the VTE group (66.7% vs 47.2%, P < 0.009). Similarly, the F2-F4 fibrosis grade had a prevalence of 58.8% in the VTE group compared to 38.5% in the non-VTE group (P < 0.006). On logistic regression, using VTE as a dependent variable, diabetes mellitus had an odds ratio (OR) =1.702 (P < 0.015), and F2-F4 fibrosis grade had an OR = 1.5 (P < 0.033).

CONCLUSION

Our analysis shows that NASH is an independent risk factor for VTE, especially deep vein thrombosis. There was a statistically significant association between the incidence of VTE, moderate-to-severe steatosis, and fibrosis. All hospitalized patients should be considered for medical thromboprophylaxis, particularly those with NASH.

Point-of-care ultrasound in geriatrics: a national survey of VA medical centers

BACKGROUND

Point-of-care ultrasound (POCUS) can aid geriatricians in caring for complex, older patients. Currently, there is limited literature on POCUS use by geriatricians. We conducted a national survey to assess current POCUS use, training desired, and barriers among Geriatrics and Extended Care ("geriatric") clinics at Veterans Affairs Medical Centers (VAMCs).

METHODS

We conducted a prospective observational study of all VAMCs between August 2019 and March 2020 using a web-based survey sent to all VAMC Chiefs of Staff and Chiefs of geriatric clinics.

RESULTS

All Chiefs of Staff (n=130) completed the survey (100% response rate). Chiefs of geriatric clinics ("chiefs") at 76 VAMCs were surveyed and 52 completed the survey (68% response rate). Geriatric clinics were located throughout the United States, mostly at high-complexity, urban VAMCs. Only 15% of chiefs responded that there was some POCUS usage in their geriatric clinic, but more than 60% of chiefs would support the implementation of POCUS use. The most common POCUS applications used in geriatric clinics were the evaluation of the bladder and urinary obstruction. Barriers to POCUS use included a lack of trained providers (56%), ultrasound equipment (50%), and funding for training (35%). Additionally, chiefs reported time utilization, clinical indications, and low patient census as barriers.

CONCLUSIONS

POCUS has several potential applications for clinicians caring for geriatric patients. Though only 15% of geriatric clinics at VAMCs currently use POCUS, most geriatric chiefs would support implementing POCUS use as a diagnostic tool. The greatest barriers to POCUS implementation in geriatric clinics were a lack of training and ultrasound equipment. Addressing these barriers systematically can facilitate implementation of POCUS use into practice and permit assessment of the impact of POCUS on geriatric care in the future.

Hyperglycemia in COVID-19 infection without diabetes mellitus: Association with inflammatory markers

BACKGROUND

New onset hyperglycemia is common in patients with severe coronavirus disease 2019 (COVID-19) infection. Cytokine storm due to COVID-19 infection is an essential etiology for new-onset hyperglycemia, but factors like direct severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-induced pancreatic β-cell failure have also been postulated to play a role.

AIM

We plan to investigate further the mechanisms underlying SARS-CoV-2 infection-induced hyperglycemia, particularly the rationale of the cytokine-induced hyperglycemia hypothesis, by evaluating the association between inflammatory markers and new onset hyperglycemia in non-diabetic patients with COVID-19 infection.

METHODS

We conducted a retrospective case-control study on adults without diabetes mellitus hospitalized for COVID-19 infection. The serum levels of glucose and inflammatory markers at presentation before initiation of corticosteroid were collected. Hyperglycemia was defined as glucose levels ≥ 140 mg/dL. C-Reactive protein (CRP) ≥ 100 mg/L, ferritin ≥ 530 ng/mL, lactate dehydrogenase (LDH) ≥ 590 U/L, and D-dimer ≥ 0.5 mg/L were considered elevated. We used the χ² test for categorical variables and the Mann-Whitney U test for continuous variables and calculated the logistic regression for hyperglycemia.

RESULTS

Of the 520 patients screened, 248 met the inclusion criteria. Baseline demographics were equally distributed between patients with hyperglycemia and those who were normoglycemic. Serum inflammatory markers in patients with or without new-onset hyperglycemia were elevated as follows: CRP (58.1% vs 65.6%, P = 0.29), ferritin (48.4% vs 34.9%, P = 0.14), D-dimer (37.1% vs 37.1%, P = 0.76) and LDH (19.4% vs 11.8%, P = 0.02). Logistic regression analysis showed LDH odds ratio (OR) = 1.623 (P = 0.256). We observed significantly higher mortality (24.2% vs 9.1%, P = 0.001; OR = 2.528, P = 0.024) and length of stay (8.89 vs 6.69, P = 0.026) in patients with hyperglycemia.

CONCLUSION

Our study showed no association between CRP, ferritin, LDH, D-dimer levels, and new-onset hyperglycemia in non-diabetic patients with COVID-19 infection. It also shows an increased mortality risk and length of stay in patients with hyperglycemia. With new-onset hyperglycemia being closely associated with poor prognostic indices, it becomes pivotal to understand the underlying pathophysiological mechanisms behind the SARS-CoV-2 infection-induced hyperglycemia. We conclude that the stress hyperglycemia hypothesis is not the only mechanism of SARS-CoV-2 infection-induced hyperglycemia but rather a multicausal pathogenesis leading to hyperglycemia that requires further research and understanding. This would help us improve not only the clinical outcomes of COVID-19 disease and inpatient hyperglycemia management but also understand the long-term effects of SARS-CoV-2 infection and further management.

Calculator for detection of colorectal adenomas by using artificial intelligence models in patients with chronic hepatitis C

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Background: Hepatitis C virus (HCV) is known for its oncogenic potential, especially in hepatocellular carcinoma and non-Hodgkin lymphoma. On review, several studies have indicated that patients with chronic hepatitis C (CHC) have an increased risk of developing colorectal cancer (CRC). We developed an artificial intelligence (AI) based tool using machine learning (ML) algorithms to help stratify these patients into a higher risk of CRC/adenomas. Methods: The study was approved by the institutional review board. We developed an AI automated calculator uploaded to a graphical user interface (GUI), and we applied ML to train models to predict the probability and the number of adenomas detected on colonoscopy. Data collected were age, smoking history, significant alcohol consumption, aspirin intake, ethnicity, HCV status, gender, body mass index (BMI), and colonoscopy findings. The models can operate either in the presence or absence of the above parameters. Data sets were split into 70:30 ratios for training and internal validation. Scikit-learn StandardScaler was used to scale values of continuous variables. We used the colonoscopy findings as the gold standard and applied a deep learning architecture to train six ML models for prediction. The ML models used were Support Vector Classifier, Random Forest, Bernoulli Naïve Bayes (BNB), Gradient Boosting Classifier (GBC), Logistic Regression, and Deep Neural Networks. Additional regression models were trained and tested to predict the number of polyps. A Flask (customizable python framework) application programming interface (API) was used to deploy the trained ML model with the highest accuracy as a web application. Finally, Heroku was used for the deployment of the web-based API to https://adenomadetection.herokuapp.com. Results: Data was collected for 415 patients, of which only 206 had colonoscopy results. On internal validation with the remaining patients, BNB predicted the probability of adenoma detection with the highest accuracy of 56%, precision of 55%, recall of 55%, and F1 measure of 54%. Support Vector Regressor (SVR) predicted the number of adenomas with the least mean absolute error (MAE) of 0.905. Conclusions: Our AI-based tool shows an association between CHC and colorectal adenomas. This tool can help providers stratify patients with CHC for early referral for screening colonoscopy. Along with giving a numerical percentage, the calculator can also comment on the number of adenomatous polyps a gastroenterologist can expect while doing a colonoscopy, thus prompting a higher adenoma detection rate.

Detection of colorectal adenomas using artificial intelligence models in patients with chronic hepatitis C

BACKGROUND

Hepatitis C virus is known for its oncogenic potential, especially in hepatocellular carcinoma and non-Hodgkin lymphoma. Several studies have shown that chronic hepatitis C (CHC) has an increased risk of the development of colorectal cancer (CRC).

AIM

To analyze this positive relationship and develop an artificial intelligence (AI)-based tool using machine learning (ML) algorithms to stratify these patient populations into risk groups for CRC/adenoma detection.

METHODS

To develop the AI automated calculator, we applied ML to train models to predict the probability and the number of adenomas detected on colonoscopy. Data sets were split into 70:30 ratios for training and internal validation. The Scikit-learn standard scaler was used to scale values of continuous variables. Colonoscopy findings were used as the gold standard and deep learning architecture was used to train six ML models for prediction. A Flask (customizable Python framework) application programming interface (API) was used to deploy the trained ML model with the highest accuracy as a web application. Finally, Heroku was used for the deployment of the web-based API to https://adenomadetection.herokuapp.com.

RESULTS

Of 415 patients, 206 had colonoscopy results. On internal validation, the Bernoulli naive Bayes model predicted the probability of adenoma detection with the highest accuracy of 56%, precision of 55%, recall of 55%, and F1 measure of 54%. Support vector regressor predicted the number of adenomas with the least mean absolute error of 0.905.

CONCLUSION

Our AI-based tool can help providers stratify patients with CHC for early referral for screening colonoscopy. Along with providing a numerical percentage, the calculator can also comment on the number of adenomatous polyps a gastroenterologist can expect, prompting a higher adenoma detection rate.

Retrospective analysis of aspirin's role in the severity of COVID-19 pneumonia

BACKGROUND

Since December 2019, an outbreak of pneumonia caused by severe acute respiratory syndrome - coronavirus-2 (SARS-CoV-2) has led to a life-threatening ongoing pandemic worldwide. A retrospective study by Chow et al showed aspirin use was associated with decreased intensive care unit (ICU) admissions in hospitalized coronavirus disease 2019 (COVID-19) patients. Recently, the RECOVERY TRIAL showed no associated reductions in the 28-d mortality or the progression to mechanical ventilation of such patients. With these conflicting findings, our study was aimed at evaluating the impact of daily aspirin intake on the outcome of COVID-19 patients.

AIM

To study was aimed at evaluating the impact of daily aspirin intake on the outcome of COVID-19 patients.

METHODS

This retrospective cohort study was conducted on 125 COVID-19 positive patients. Subgroup analysis to evaluate the association of demographics and comorbidities was undertaken. The impact of chronic aspirin use was assessed on the survival outcomes, need for mechanical ventilation, and progression to ICU. Variables were evaluated using the chi-square test and multinomial logistic regression analysis.

RESULTS

125 patients were studied, 30.40% were on daily aspirin, and 69.60% were not. Cross-tabulation of the clinical parameters showed that hypertension (P = 0.004), hyperlipidemia (0.016), and diabetes mellitus (P = 0.022) were significantly associated with aspirin intake. Regression analysis for progression to the ICU, need for mechanical ventilation and survival outcomes against daily aspirin intake showed no statistical significance.

CONCLUSION

Our study suggests that daily aspirin intake has no protective impact on COVID-19 illness-associated survival outcomes, mechanical ventilation, or progression to ICU level of care.

Glycogen hepatopathy in type-1 diabetes mellitus: A case report

BACKGROUND

It has been studied that fluctuating glucose levels may superimpose glycated hemoglobin in determining the risk for diabetes mellitus (DM) complications. While non-alcoholic steatohepatitis (NASH) remains a predominant cause of elevated transaminases in Type 2 DM due to a strong underplay of metabolic syndrome, Type 1 DM can contrastingly affect the liver in a direct, benign, and reversible manner, causing Glycogen hepatopathy (GH) - with a good prognosis.

CASE SUMMARY

A 50-year-old female with history of poorly controlled type 1 DM, status post cholecystectomy several years ago, and obesity presented with nausea, vomiting, and abdominal pain. Her vitals at the time of admission were stable. On physical examination, she had diffuse abdominal tenderness. Her finger-stick glucose was 612 mg/dL with elevated ketones and low bicarbonate. Her labs revealed abnormal liver studies: AST 1460 U/L, ALP: 682 U/L, ALP: 569 U/L, total bilirubin: 0.3mg/dL, normal total protein, albumin, and prothrombin time/ international normalized ratio (PT/INR). A magnetic resonance cholangiopancreatography (MRCP) demonstrated mild intra and extra-hepatic biliary ductal dilation without evidence of choledocholithiasis. She subsequently underwent a diagnostic ERCP which showed a moderately dilated CBD, for which a stent was placed. Studies for viral hepatitis, Wilson’s Disease, alpha-1-antitrypsin, and iron panel came back normal. Due to waxing and waning transaminases during the hospital course, a liver biopsy was eventually done, revealing slightly enlarged hepatocytes that were PAS-positive, suggestive of glycogenic hepatopathy. With treatment of hyperglycemia and ensuing strict glycemic control, her transaminases improved, and she was discharged.

CONCLUSION

With a negative hepatocellular and cholestatic work-up, our patient likely had GH, a close differential for NASH but a poorly recognized entity. GH, first described in 1930 as a component of Mauriac syndrome, is believed to be due to glucose and insulin levels fluctuation. Dual echo magnetic resonance imaging sequencing and computed tomography scans of the liver are helpful to differentiate GH from NASH. Still, liver biopsy remains the gold standard for diagnosis. Biopsy predominantly shows intra-cellular glycogen deposition, with minimal or no steatosis or inflammation. As GH is reversible with good glycemic control, it should be one of the differentials in patients with brittle diabetes and elevated transaminases. GH, however, can cause a dramatic elevation in transaminases (50-1600 IU/L) alongside hepatomegaly and abdominal pain that would raise concern for acute liver injury leading to exhaustive work-up, as was in our patient above. Fluctuation in transaminases is predominantly seen during hyperglycemic episodes, and proper glycemic control is the mainstay of the treatment.