Intracardiac Thrombus in COVID-19 Inpatients: A Nationwide Study of Incidence, Predictors, and Outcomes.

COronaVIrus Disease-2019 (COVID-19) is associated with a hypercoagulable state. Intracardiac thrombosis is a potentially serious complication but has seldom been evaluated in COVID-19 patients. We assessed the incidence, associated factors, and outcomes of COVID-19 patients with intracardiac thrombosis. In 2020, COVID-19 inpatients were identified from the National Inpatient Sample (NIS) database. Data on clinical characteristics, intracardiac thrombosis, and adverse outcomes were collected. Multivariable logistic regression was used to identify factors associated with intracardiac thrombosis, in-hospital mortality, and morbidities. In 2020, 1,683,785 COVID-19 inpatients (mean age 63.8 years, 32.2% females) were studied. Intracardiac thrombosis occurred in 0.10% (1830) of cases. In-hospital outcomes included 13.2% all-cause mortality, 3.5% cardiovascular mortality, 2.6% cardiac arrest, 4.4% acute coronary syndrome (ACS), 16.1% heart failure, 1.3% stroke, and 28.3% acute kidney injury (AKI). Key factors for intracardiac thrombosis were congestive heart failure history and coagulopathy. Intracardiac thrombosis independently linked to higher risks of all-cause mortality (odds ratio [OR]: 3.32 (2.42-4.54)), cardiovascular mortality (OR: 2.95 (1.96-4.44)), cardiac arrest (OR: 2.04 (1.22-3.43)), ACS (OR: 1.62 (1.17-2.22)), stroke (OR: 3.10 (2.11-4.56)), and AKI (OR: 2.13 (1.68-2.69)), but not heart failure. While rare, intracardiac thrombosis in COVID-19 patients independently raised in-hospital mortality and morbidity risks.

MitraCox Score Predicts In-Hospital Mortality in Patients Admitted for Transcatheter Edge-to-Edge Mitral Valve Repair.

As mitral valve transcatheter edge-to-edge repair (M-TEER) is evolving as an effective treatment for high-risk surgical patients with mitral regurgitation, there is a pressing need for cardiologists to optimize resources through risk stratification of in-hospital mortality for this patient population. Although current risk-prediction models have been shown to predict adverse outcomes with reasonable accuracy, models trained using the US nationwide population are lacking. This study aimed to identify clinical, demographic, and procedural features that predict in-hospital mortality, and to derive and validate an in-hospital mortality risk-prediction tool in patients who have undergone M-TEER. A total of 9,373 admissions from the Nationwide Readmissions Database of patients who underwent M-TEER between 2015 and 2018 were used to develop and validate the model. We first performed least absolute shrinkage and selection operator (LASSO) regularization of Cox regression (Coxnet) that is 10-fold cross-validated. The non-zero coefficients were multiplied with the respective values of each observation of the predictors to build the scoring formula. Out of 9,373 admissions, 196 patients (2%) died in-hospital during index admission. In descending order, the top variables that were most predictive of in-hospital mortality were higher age, presence of fluid and electrolyte disturbance, and large metropolitan location of the hospital. The validation C-statistic of the MitraCox score was 0.82. Using X-tile software (Yale School of Medicine, New Haven, Connecticut), 2 cutoffs of the score were determined on the basis of in-hospital mortality and length of stay, and the survival of the population was classified into 3 risk groups: low, intermediate, and high. The scoring system deployed online as a web-based calculator can be accessed at https://kathavs.shinyapps.io/Mitracox_Kapadia/. In conclusion, MitraCox score is easy to calculate and predicts in-hospital mortality depending on length of stay in a dynamic manner.

Defecatory disorders in patients with type 1 diabetes and chronic constipation.

BACKGROUND AND OBJECTIVES: Chronic constipation (CC) may be caused by defecatory disorders (DDs) and associated with reduced rectal sensation. Among patients with type 1 diabetes (T1D) and CC (T1DCC patients), the prevalence of DDs and reduced rectal sensation is unknown. We sought to compare complications of T1D, anorectal dysfunction, and CC symptoms, among T1DCC patients with versus without a DD. METHODS: Anorectal pressures at rest and during squeeze and evacuation, as well as rectal sensation and rectal balloon expulsion time (BET) were measured with high-resolution anorectal manometry in 114 consecutive T1DCC patients. RESULTS: Thirty-seven patients (32%) had prolonged BET, suggestive of a DD. Complications of T1D included peripheral neuropathy (n = 67, 59%), retinopathy (n = 42, 37%), and nephropathy (n = 26, 23%). Among these complications, only retinopathy was associated with, that is, more prevalent in patients with normal (45%) than prolonged BET (19%). Compared with patients with normal BET, patients with prolonged BET had a lower rectal pressure (mean [SD], 32 [23] mm Hg vs. 23 [19] mm Hg, p = 0.03), greater anal pressure (91 [23] mm Hg vs. 68 [36] mm Hg, p < 0.001), and lower rectoanal gradient (-67 [30] mm Hg vs. -36 [32] mm Hg, p < 0.0001) during evacuation. Anal resting pressure and anal squeeze increment were below normal in 14 (13%) and 32 (29%) of patients and one or more rectal sensory thresholds were above normal in 34 (30%) patients; these abnormalities affected similar proportions in the normal and prolonged BET cohorts. CONCLUSIONS: Among T1DCC patients, 37 (32%) had prolonged BET, which was associated with anorectal pressures indicative of a DD but was not associated with reduced rectal sensation, suggesting that DDs are more likely explained by abdomino-anal dyscoordination than visceral disturbance.

Comparison of Anorectal Manometry, Rectal Balloon Expulsion Test, and Defecography for Diagnosing Defecatory Disorders.

BACKGROUND & AIMS: The utility of high-resolution anorectal manometry (HR-ARM) for diagnosing defecatory disorders (DDs) is unclear because healthy people may have features of dyssynergia. We aimed to identify objective diagnostic criteria for DD and to ascertain the utility of HR-ARM for diagnosing DD. METHODS: Constipated patients were assessed with HR-ARM and rectal balloon expulsion time (BET), and a subset underwent defecography. Normal values were established by assessing 184 sex-matched healthy individuals. Logistic regression models evaluated the association of abnormal HR-ARM findings with prolonged BET and reduced rectal evacuation (determined by defecography). RESULTS: A total of 474 constipated individuals (420 women) underwent HR-ARM and BET, and 158 underwent defecography. BET was prolonged, suggesting a DD, for 152 patients (32%). Rectal evacuation was lower for patients with prolonged vs normal BET. A lower rectoanal gradient during evacuation, reduced anal squeeze increment, and reduced rectal sensation were independently associated with abnormal BETs; the rectoanal gradient was 36% sensitive and 85% specific for prolonged BET. A lower rectoanal gradient and prolonged BET were independently associated with reduced evacuation. Among constipated patients, the probability of reduced rectal evacuation was 14% when the gradient and BET were both normal, 45% when either was abnormal, and 75% when both variables were abnormal. CONCLUSIONS: HR-ARM, BET, and defecography findings were concordant for constipated patients, and reduced rectoanal gradient was the best HR-ARM predictor of prolonged BET or reduced rectal evacuation. Prolonged BET, reduced gradient, and reduced evacuation each independently supported a diagnosis of DD in constipated patients. We propose the terms probable DD for patients with an isolated abnormal gradient or BET and definite DD for patients with abnormal results from both tests.

Optimizing techniques for measuring anal resting and squeeze pressures with high-resolution manometry.

BACKGROUND: The optimal methods for measuring and analyzing anal resting and squeeze pressure with high-resolution manometry (HRM) are unclear. METHODS: Anal resting and squeeze pressures were measured with HRM in 90 healthy women, 35 women with defecatory disorders (DD), and 85 with fecal incontinence (FI). Pressures were analyzed with Manoview™ software and a customized approach. Resting pressures measured for 20, 60, and 300 s were compared. During the squeeze period, (3 maneuvers, 20 s each), the squeeze increment, which was averaged over 5, 10, 15, and 20 s, and squeeze duration were evaluated. RESULTS: Compared to healthy women, the anal resting pressure, squeeze pressure increment, and squeeze duration were lower in FI (p ≤ 0.04) but not in DD. The 20, 60, and 300 s resting pressures were strongly correlated (concordance correlation coefficients = 0.96-0.99) in healthy and DD women. The 5 s squeeze increment was the greatest; 10, 15, and 20 s values were progressively lower (p < 0.001). The squeeze pressure increment and duration differed (p < 0.01) among the three maneuvers in healthy and DD women but not in FI women. The upper 95th percentile limit for squeeze duration was 19.5 s in controls, 19.9 s in DD, and 19.3 s in FI. Adjusted for age, resting pressure, and squeeze duration, a greater squeeze increment was associated with a lower risk of FI versus health (OR, 0.96; 95% CI, 0.94-0.97). CONCLUSIONS: These findings suggest that anal resting and squeeze pressures can be accurately measured over 20 s. In most patients, one squeeze maneuver is probably sufficient.

Neural Network Pattern Recognition of Ultrasound Image Gray Scale Intensity Histograms of Breast Lesions to Differentiate Between Benign and Malignant Lesions: Analytical Study.

BACKGROUND: Ultrasound-based radiomic features to differentiate between benign and malignant breast lesions with the help of machine learning is currently being researched. The mean echogenicity ratio has been used for the diagnosis of malignant breast lesions. However, gray scale intensity histogram values as a single radiomic feature for the detection of malignant breast lesions using machine learning algorithms have not been explored yet. OBJECTIVE: This study aims to assess the utility of a simple convolutional neural network in classifying benign and malignant breast lesions using gray scale intensity values of the lesion. METHODS: An open-access online data set of 200 ultrasonogram breast lesions were collected, and regions of interest were drawn over the lesions. The gray scale intensity values of the lesions were extracted. An input file containing the values and an output file consisting of the breast lesions' diagnoses were created. The convolutional neural network was trained using the files and tested on the whole data set. RESULTS: The trained convolutional neural network had an accuracy of 94.5% and a precision of 94%. The sensitivity and specificity were 94.9% and 94.1%, respectively. CONCLUSIONS: Simple neural networks, which are cheap and easy to use, can be applied to diagnose malignant breast lesions with gray scale intensity values obtained from ultrasonogram images in low-resource settings with minimal personnel.