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Hospitalised COVID-19 patients of the Mount Sinai Health System: a retrospective observational study using the electronic medical records.

ABSTRACT: OBJECTIVE:To assess association of clinical features on COVID-19 patient outcomes. DESIGN:Retrospective observational study using electronic medical record data. SETTING:Five member hospitals from the Mount Sinai Health System in New York City (NYC). PARTICIPANTS:28?336 patients tested for SARS-CoV-2 from 24 February 2020 to 15 April 2020, including 6158 laboratory-confirmed COVID-19 cases. MAIN OUTCOMES AND MEASURES:Positive test rates and in-hospital mortality were assessed for different racial groups. Among positive cases admitted to the hospital (N=3273), we estimated HR for both discharge and death across various explanatory variables, including patient demographics, hospital site and unit, smoking status, vital signs, lab results and comorbidities. RESULTS:Hispanics (29%) and African Americans (25%) had disproportionately high positive case rates relative to their representation in the overall NYC population (p<0.05); however, no differences in mortality rates were observed in hospitalised patients based on race. Outcomes differed significantly between hospitals (Gray's T=248.9; p<0.05), reflecting differences in average baseline age and underlying comorbidities. Significant risk factors for mortality included age (HR 1.05, 95% CI 1.04 to 1.06; p=1.15e-32), oxygen saturation (HR 0.985, 95% CI 0.982 to 0.988; p=1.57e-17), care in intensive care unit areas (HR 1.58, 95% CI 1.29 to 1.92; p=7.81e-6) and elevated creatinine (HR 1.75, 95% CI 1.47 to 2.10; p=7.48e-10), white cell count (HR 1.02, 95% CI 1.01 to 1.04; p=8.4e-3) and body mass index (BMI) (HR 1.02, 95% CI 1.00 to 1.03; p=1.09e-2). Deceased patients were more likely to have elevated markers of inflammation. CONCLUSIONS:While race was associated with higher risk of infection, we did not find racial disparities in inpatient mortality suggesting that outcomes in a single tertiary care health system are comparable across races. In addition, we identified key clinical features associated with reduced mortality and discharge. These findings could help to identify which COVID-19 patients are at greatest risk of a severe infection response and predict survival.

SUBMITTER: Wang Z

PROVIDER: S-EPMC7592304 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Hospitalised COVID-19 patients of the Mount Sinai Health System: a retrospective observational study using the electronic medical records.

Wang Zichen Z Zheutlin Amanda A Kao Yu-Han YH Ayers Kristin K Gross Susan S Kovatch Patricia P Nirenberg Sharon S Charney Alexander A Nadkarni Girish G De Freitas Jessica K JK O'Reilly Paul P Just Allan A Horowitz Carol C Martin Glenn G Branch Andrea A Glicksberg Benjamin S BS Charney Dennis D Reich David D Oh William K WK Schadt Eric E Chen Rong R Li Li L

BMJ open 20201026 10

<h4>Objective</h4>To assess association of clinical features on COVID-19 patient outcomes.<h4>Design</h4>Retrospective observational study using electronic medical record data.<h4>Setting</h4>Five member hospitals from the Mount Sinai Health System in New York City (NYC).<h4>Participants</h4>28 336 patients tested for SARS-CoV-2 from 24 February 2020 to 15 April 2020, including 6158 laboratory-confirmed COVID-19 cases.<h4>Main outcomes and measures</h4>Positive test rates and in-hospital mortali ...[more]

PMID: 33109676

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Project description:Reduction of preventable hospital readmissions that result from chronic or acute conditions like stroke, heart failure, myocardial infarction and pneumonia remains a significant challenge for improving the outcomes and decreasing the cost of healthcare delivery in the United States. Patient readmission rates are relatively high for conditions like heart failure (HF) despite the implementation of high-quality healthcare delivery operation guidelines created by regulatory authorities. Multiple predictive models are currently available to evaluate potential 30-day readmission rates of patients. Most of these models are hypothesis driven and repetitively assess the predictive abilities of the same set of biomarkers as predictive features. In this manuscript, we discuss our attempt to develop a data-driven, electronic-medical record-wide (EMR-wide) feature selection approach and subsequent machine learning to predict readmission probabilities. We have assessed a large repertoire of variables from electronic medical records of heart failure patients in a single center. The cohort included 1,068 patients with 178 patients were readmitted within a 30-day interval (16.66% readmission rate). A total of 4,205 variables were extracted from EMR including diagnosis codes (n=1,763), medications (n=1,028), laboratory measurements (n=846), surgical procedures (n=564) and vital signs (n=4). We designed a multistep modeling strategy using the Naïve Bayes algorithm. In the first step, we created individual models to classify the cases (readmitted) and controls (non-readmitted). In the second step, features contributing to predictive risk from independent models were combined into a composite model using a correlation-based feature selection (CFS) method. All models were trained and tested using a 5-fold cross-validation method, with 70% of the cohort used for training and the remaining 30% for testing. Compared to existing predictive models for HF readmission rates (AUCs in the range of 0.6-0.7), results from our EMR-wide predictive model (AUC=0.78; Accuracy=83.19%) and phenome-wide feature selection strategies are encouraging and reveal the utility of such datadriven machine learning. Fine tuning of the model, replication using multi-center cohorts and prospective clinical trial to evaluate the clinical utility would help the adoption of the model as a clinical decision system for evaluating readmission status.

Project description:BackgroundFew studies have used standardized nursing records with Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) to identify predictors of clinical deterioration.ObjectiveThis study aims to standardize the nursing documentation records of patients with COVID-19 using SNOMED CT and identify predictive factors of clinical deterioration in patients with COVID-19 via standardized nursing records.MethodsIn this study, 57,558 nursing statements from 226 patients with COVID-19 were analyzed. Among these, 45,852 statements were from 207 patients in the stable (control) group and 11,706 from 19 patients in the exacerbated (case) group who were transferred to the intensive care unit within 7 days. The data were collected between December 2019 and June 2022. These nursing statements were standardized using the SNOMED CT International Edition released on November 30, 2022. The 260 unique nursing statements that accounted for the top 90% of 57,558 statements were selected as the mapping source and mapped into SNOMED CT concepts based on their meaning by 2 experts with more than 5 years of SNOMED CT mapping experience. To identify the main features of nursing statements associated with the exacerbation of patient condition, random forest algorithms were used, and optimal hyperparameters were selected for nursing problems or outcomes and nursing procedure-related statements. Additionally, logistic regression analysis was conducted to identify features that determine clinical deterioration in patients with COVID-19.ResultsAll nursing statements were semantically mapped to SNOMED CT concepts for "clinical finding," "situation with explicit context," and "procedure" hierarchies. The interrater reliability of the mapping results was 87.7%. The most important features calculated by random forest were "oxygen saturation below reference range," "dyspnea," "tachypnea," and "cough" in "clinical finding," and "oxygen therapy," "pulse oximetry monitoring," "temperature taking," "notification of physician," and "education about isolation for infection control" in "procedure." Among these, "dyspnea" and "inadequate food diet" in "clinical finding" increased clinical deterioration risk (dyspnea: odds ratio [OR] 5.99, 95% CI 2.25-20.29; inadequate food diet: OR 10.0, 95% CI 2.71-40.84), and "oxygen therapy" and "notification of physician" in "procedure" also increased the risk of clinical deterioration in patients with COVID-19 (oxygen therapy: OR 1.89, 95% CI 1.25-3.05; notification of physician: OR 1.72, 95% CI 1.02-2.97).ConclusionsThe study used SNOMED CT to express and standardize nursing statements. Further, it revealed the importance of standardized nursing records as predictive variables for clinical deterioration in patients.

Dataset Information

Hospitalised COVID-19 patients of the Mount Sinai Health System: a retrospective observational study using the electronic medical records.

Publications

Hospitalised COVID-19 patients of the Mount Sinai Health System: a retrospective observational study using the electronic medical records.

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