scholarly journals Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19

2021 ◽  
pp. CJN.17311120
Author(s):  
Akhil Vaid ◽  
Lili Chan ◽  
Kumardeep Chaudhary ◽  
Suraj Jaladanki ◽  
Ishan Paranjpe ◽  
...  
Keyword(s):  
Hospital Admission ◽  
Time Windows ◽  
External Validation ◽  
Kidney Injury ◽  
Mount Sinai Hospital ◽  
Mean Difference ◽  
Internal Validation ◽  
Distribution Width ◽  
Mount Sinai ◽  
Predictive Approaches

Background: Acute Kidney Injury treated with dialysis initiation is a common complication of COVID-19 infection among hospitalized patients. However, dialysis supplies and personnel are often limited. Methods: Using data from adult hospitalized COVID-19 patients from five hospitals from the Mount Sinai Health System who were admitted from March 10th and December 26th, 2020, we developed and validated several models (logistic regression, LASSO, random forest, and XGBoost (with and without imputation)) for predicting treatment with dialysis or death at various time horizons (1, 3, 5 and 7 days) following hospital admission. Patients admitted to the Mount Sinai Hospital were used for internal validation while the other hospitals were part of the external validation cohort. Features included demographics, comorbidities, and laboratory and vitals signs within 12 hour of hospital admission. Results: 6093 patients (2,442 in training and 3,651 in external validation) were included in the final cohort. Of the different model approaches used, XGBoost without imputation had the highest area under the receiver curve on internal validation (range of 0.93-0.98) and area under the precision recall curve (range of 0.78-0.82 across) for all time points. XGBoost without imputation also had the highest test parameters on external validation (AUROC range 0.85 to 0.87, and AUPRC range of 0.27 to 0.54) across all time windows. XGBoost without imputation outperformed all models with higher precision and recall (mean difference in AUROC of 0.04 and mean difference in AUPRC of 0.15). Features of creatinine, Blood Urea Nitrogen, and Red cell distribution width were major drivers of model's prediction. Conclusions: An XGBoost model without imputation for prediction of a composite outcome of either death or dialysis in COVID positive patients had the best performance compared to standard and other machine learning models.

scholarly journals Neonatal outcomes during the COVID-19 pandemic in New York City

2020 ◽  
Author(s):  
Felix Richter ◽  
Arielle S. Strasser ◽  
Mayte Suarez-Farinas ◽  
Shan Zhao ◽  
Girish N. Nadkarni ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Neonatal Intensive Care ◽  
Time Window ◽  
Time Windows ◽  
The United States ◽  
Mount Sinai Hospital ◽  
Premature Births ◽  
Mount Sinai

ABSTRACTWe explored rates of premature births and neonatal intensive care unit (NICU) admissions at the Mount Sinai Hospital after the implementation of COVID-19 lockdown measures (March 16, 2020) and phase one reopening (June 8, 2020), comparing them to those of the same time periods from 2012-2019. Mount Sinai Hospital is in New York City (NYC), an early epicenter of COVID-19 in the United States, which was heavily impacted by the pandemic during the study period. Among 43,963 singleton births, we observed no difference in either outcome after the implementation of lockdown measures when compared to the same trends in prior years (p=0.09-0.35). Of interest, we observed a statistically significant decrease in premature births after NYC phase one reopening compared to those of the same time period in 2012-2019 across all time windows (p=0.0028-0.049), and a statistically significant decrease in NICU admissions over the largest time window (2.75 months) compared to prior years (p=0.0011).

scholarly journals LUCAS: A highly accurate yet simple risk calculator that predicts survival of COVID-19 patients using rapid routine tests

2021 ◽  
Author(s):  
Surajit Ray ◽  
Andrew Swift ◽  
Joey W Fanstone ◽  
Abhirup Banerjee ◽  
Michail Mamalakis ◽  
...  
Keyword(s):  
Hospital Admission ◽  
Validation Cohort ◽  
Point Of Care ◽  
External Validation ◽  
Fatal Outcome ◽  
Image Data ◽  
Discriminatory Power ◽  
Validation Dataset ◽  
Data Set ◽  
Internal Validation

There is an urgent need to develop a simplified risk tool that enables rapid triaging of SARS CoV-2 positive patients during hospital admission, which complements current practice. Many predictive tools developed to date are complex, rely on multiple blood results and past medical history, do not include chest X ray results and rely on Artificial Intelligence rather than simplified algorithms. Our aim was to develop a simplified risk-tool based on five parameters and CXR image data that predicts the 60-day survival of adult SARS CoV-2 positive patients at hospital admission. Methods We analysed the NCCID database of patient blood variables and CXR images from 19 hospitals across the UK contributed clinical data on SARS CoV-2 positive patients using multivariable logistic regression. The initial dataset was non-randomly split between development and internal validation dataset with 1434 and 310 SARS CoV-2 positive patients, respectively. External validation of final model conducted on 741 Accident and Emergency admissions with suspected SARS CoV-2 infection from a separate NHS Trust which was not part of the initial NCCID data set. Findings The LUCAS mortality score included five strongest predictors (lymphocyte count, urea, CRP, age, sex), which are available at any point of care with rapid turnaround of results. Our simple multivariable logistic model showed high discrimination for fatal outcome with the AUC-ROC in development cohort 0.765 (95% confidence interval (CI): 0.738 - 0.790), in internal validation cohort 0.744 (CI: 0.673 - 0.808), and in external validation cohort 0.752 (CI: 0.713 - 0.787). The discriminatory power of LUCAS mortality score was increased slightly when including the CXR image data (for normal versus abnormal): internal validation AUC-ROC 0.770 (CI: 0.695 - 0.836) and external validation AUC-ROC 0.791 (CI: 0.746 - 0.833). The discriminatory power of LUCAS and LUCAS + CXR performed in the upper quartile of pre-existing risk stratification scores with the added advantage of using only 5 predictors. Interpretation This simplified prognostic tool derived from objective parameters can be used to obtain valid predictions of mortality in patients within 60 days SARS CoV-2 RT-PCR results. This free-to-use simplified tool can be used to assist the triage of patients into low, moderate, high or very high risk of fatality and is available at https://mdscore.net/.

QSAR Analysis for Antioxidant Activity of Dipicolinic Acid Derivatives

2018 ◽  
Vol 21 (3) ◽  
pp. 204-214 ◽  
Author(s):  
Vesna Rastija ◽  
Maja Molnar ◽  
Tena Siladi ◽  
Vijay Hariram Masand
Keyword(s):  
Antioxidant Activity ◽  
Antioxidant Activities ◽  
Dipicolinic Acid ◽  
External Validation ◽  
Qsar Model ◽  
Internal Validation ◽  
Qsar Analysis ◽  
Dragon Descriptors ◽  
Qsar Models ◽  
New Compounds

Aims and Objectives: The aim of this study was to derive robust and reliable QSAR models for clarification and prediction of antioxidant activity of 43 heterocyclic and Schiff bases dipicolinic acid derivatives. According to the best obtained QSAR model, structures of new compounds with possible great activities should be proposed. Methods: Molecular descriptors were calculated by DRAGON and ADMEWORKS from optimized molecular structure and two algorithms were used for creating the training and test sets in both set of descriptors. Regression analysis and validation of models were performed using QSARINS. Results: The model with best internal validation result was obtained by DRAGON descriptors (MATS4m, EEig03d, BELm4, Mor10p), split by ranking method (R2 = 0.805; R2 ext = 0.833; F = 30.914). The model with best external validation result was obtained by ADMEWORKS descriptors (NDB, MATS5p, MDEN33, TPSA), split by random method (R2 = 0.692; R2 ext = 0.848; F = 16.818). Conclusion: Important structural requirements for great antioxidant activity are: low number of double bonds in molecules; absence of tertial nitrogen atoms; higher number of hydrogen bond donors; enhanced molecular polarity; and symmetrical moiety. Two new compounds with potentially great antioxidant activities were proposed.

scholarly journals Multiplex Assays of Luminex for Identification of COVID-19 Antibodies in Patient Serum: Performance Evaluation and Comparison to ELISA

2020 ◽  
Vol 154 (Supplement_1) ◽  
pp. S92-S92
Author(s):  
M S Shapiro ◽  
X Wang ◽  
D R Mendu ◽  
A Firpo
Keyword(s):  
Dynamic Range ◽  
High Titer ◽  
Mount Sinai Hospital ◽  
Antibody Testing ◽  
Luminex Assay ◽  
Multiplex Assays ◽  
Negative Results ◽  
Broad Dynamic Range ◽  
Circulating Antibodies ◽  
Mount Sinai

Abstract Introduction/Objective Mount Sinai Hospital has received emergency use authorization (EUA) from the FDA for Coronavirus Disease 2019 (COVID-19) antibody testing using ELISA. This serological assay detects and titrates the presence of circulating antibodies to COVID-19. Other platforms have aimed to achieve the credentials of the ELISA instrument, including the multiplex assays of Luminex. The platform is known to have a greater throughput (384 wells vs. 96 wells per microplate) and faster processing speed (8 hours vs. 17 hours). Methods Luminex utilizes beads that couple to the same COVID-19 antigens (mRBD and mSpike) which were utilized for the ELISA assay. The beads are read determining the mean fluorescence intensity (MFI). In order to compare the two methods, our study included 61 patients with COVID-19 at Mount Sinai Hospital, to screen and titrate their sera using Luminex, and to correspond the MFI values with the ELISA titers. Results The Luminex assay has achieved the same level of confidence as ELISA. The 61 patients, representing 30 negatives and 31 positives, are consistently identified as such on both platforms. Our data highlights 32% of patients with a low titer (<1:160), 42% of patients with a high titer (1:160 ~ 1:320), and 26% of patients with a very high titer level (>1:320). These titers correlated well with the MFI values. Based on a cutoff of 80,000 MFI, the sensitivity and specificity of the assay is 98% and 85%, respectively, with no overlapping of MFI between positive and negative results. Conclusion Overall, the study has demonstrated that the Luminex is a strong alternative for the ELISA platform. The Luminex highlights the broad dynamic range with no overlapping between positives and negatives. Migration from ELISA to Luminex, a platform with faster and greater throughput, is therefore, highly desirable.

scholarly journals Modification and Validation of the Phosphate Removal Model: A Multicenter Study

2021 ◽  
pp. 1-10
Author(s):  
Weichen Zhang ◽  
Qiuna Du ◽  
Jing Xiao ◽  
Zhaori Bi ◽  
Chen Yu ◽  
...  
Keyword(s):  
Polynomial Regression ◽  
External Validation ◽  
Original Model ◽  
Phosphate Concentration ◽  
Phosphate Removal ◽  
Internal Validation ◽  
Modified Model ◽  
New Model ◽  
Significant Difference ◽  
Removal Model

<b><i>Background:</i></b> Our research group has previously reported a noninvasive model that estimates phosphate removal within a 4-h hemodialysis (HD) treatment. The aim of this study was to modify the original model and validate the accuracy of the new model of phosphate removal for HD and hemodiafiltration (HDF) treatment. <b><i>Methods:</i></b> A total of 109 HD patients from 3 HD centers were enrolled. The actual phosphate removal amount was calculated using the area under the dialysate phosphate concentration time curve. Model modification was executed using second-order multivariable polynomial regression analysis to obtain a new parameter for dialyzer phosphate clearance. Bias, precision, and accuracy were measured in the internal and external validation to determine the performance of the modified model. <b><i>Results:</i></b> Mean age of the enrolled patients was 63 ± 12 years, and 67 (61.5%) were male. Phosphate removal was 19.06 ± 8.12 mmol and 17.38 ± 6.75 mmol in 4-h HD and HDF treatments, respectively, with no significant difference. The modified phosphate removal model was expressed as Tpo<sub>4</sub> = 80.3 × <i>C</i><sub>45</sub> − 0.024 × age + 0.07 × weight + β × clearance − 8.14 (β = 6.231 × 10<sup>−3</sup> × clearance − 1.886 × 10<sup>−5</sup> × clearance<sup>2</sup> – 0.467), where <i>C</i><sub>45</sub> was the phosphate concentration in the spent dialysate measured at the 45th minute of HD and clearance was the phosphate clearance of the dialyzer. Internal validation indicated that the new model was superior to the original model with a significantly smaller bias and higher accuracy. External validation showed that <i>R</i><sup>2</sup>, bias, and accuracy were not significantly different than those of internal validation. <b><i>Conclusions:</i></b> A new model was generated to quantify phosphate removal by 4-h HD and HDF with a dialyzer surface area of 1.3–1.8 m<sup>2</sup>. This modified model would contribute to the evaluation of phosphate balance and individualized therapy of hyperphosphatemia.

scholarly journals Introducing Twitter Daily Estimates of Residents and Non-Residents at the County Level

2021 ◽  
Vol 10 (6) ◽  
pp. 227
Author(s):  
Yago Martín ◽  
Zhenlong Li ◽  
Yue Ge ◽  
Xiao Huang
Keyword(s):  
Large Scale ◽  
Local Population ◽  
External Validation ◽  
Population Estimates ◽  
County Level ◽  
Internal Validation ◽  
Population Movements ◽  
Dynamic Component ◽  
Home Location ◽  
Non Local

The study of migrations and mobility has historically been severely limited by the absence of reliable data or the temporal sparsity of available data. Using geospatial digital trace data, the study of population movements can be much more precisely and dynamically measured. Our research seeks to develop a near real-time (one-day lag) Twitter census that gives a more temporally granular picture of local and non-local population at the county level. Internal validation reveals over 80% accuracy when compared with users’ self-reported home location. External validation results suggest these stocks correlate with available statistics of residents/non-residents at the county level and can accurately reflect regular (seasonal tourism) and non-regular events such as the Great American Solar Eclipse of 2017. The findings demonstrate that Twitter holds the potential to introduce the dynamic component often lacking in population estimates. This study could potentially benefit various fields such as demography, tourism, emergency management, and public health and create new opportunities for large-scale mobility analyses.

scholarly journals Automatized Hepatic Tumor Volume Analysis of Neuroendocrine Liver Metastases by Gd-EOB MRI—A Deep-Learning Model to Support Multidisciplinary Cancer Conference Decision-Making

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2726
Author(s):  
Uli Fehrenbach ◽  
Siyi Xin ◽  
Alexander Hartenstein ◽  
Timo Alexander Auer ◽  
Franziska Dräger ◽  
...  
Keyword(s):  
Liver Metastases ◽  
External Validation ◽  
High Accuracy ◽  
Hepatic Tumor ◽  
Liver Malignancies ◽  
Internal Validation ◽  
Matthew’S Correlation Coefficient ◽  
Therapy Success ◽  
Neuroendocrine Liver Metastases

Background: Rapid quantification of liver metastasis for diagnosis and follow-up is an unmet medical need in patients with secondary liver malignancies. We present a 3D-quantification model of neuroendocrine liver metastases (NELM) using gadoxetic-acid (Gd-EOB)-enhanced MRI as a useful tool for multidisciplinary cancer conferences (MCC). Methods: Manual 3D-segmentations of NELM and livers (149 patients in 278 Gd-EOB MRI scans) were used to train a neural network (U-Net architecture). Clinical usefulness was evaluated in another 33 patients who were discussed in our MCC and received a Gd-EOB MRI both at baseline and follow-up examination (n = 66) over 12 months. Model measurements (NELM volume; hepatic tumor load (HTL)) with corresponding absolute (ΔabsNELM; ΔabsHTL) and relative changes (ΔrelNELM; ΔrelHTL) between baseline and follow-up were compared to MCC decisions (therapy success/failure). Results: Internal validation of the model’s accuracy showed a high overlap for NELM and livers (Matthew’s correlation coefficient (φ): 0.76/0.95, respectively) with higher φ in larger NELM volume (φ = 0.80 vs. 0.71; p = 0.003). External validation confirmed the high accuracy for NELM (φ = 0.86) and livers (φ = 0.96). MCC decisions were significantly differentiated by all response variables (ΔabsNELM; ΔabsHTL; ΔrelNELM; ΔrelHTL) (p < 0.001). ΔrelNELM and ΔrelHTL showed optimal discrimination between therapy success or failure (AUC: 1.000; p < 0.001). Conclusion: The model shows high accuracy in 3D-quantification of NELM and HTL in Gd-EOB-MRI. The model’s measurements correlated well with MCC’s evaluation of therapeutic response.

Neurosurgery at the Mount Sinai Hospital

1994 ◽  
Vol 80 (5) ◽  
pp. 935-938 ◽  
Author(s):  
Jeffrey S. Oppenheim
Keyword(s):  
Mount Sinai Hospital ◽  
Content Type ◽  
History Of Neurosurgery ◽  
History Of ◽  
Mount Sinai ◽  
Fine Print

✓ The Mount Sinai Hospital was founded in 1852 under the name “The Jews' Hospital.” Neurosurgery at Mount Sinai Hospital can be traced to the work of Dr. Charles Elsberg. In 1932, the Department of Neurosurgery was created under the direction of Dr. Ira Cohen. The history of neurosurgery at the Mount Sinai Hospital is recounted.

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