scholarly journals Development of an Automated, Real Time Surveillance Tool for Predicting Readmissions at a Community Hospital

2013 ◽  
Vol 04 (02) ◽  
pp. 153-169 ◽  
Author(s):  
R. Gildersleeve ◽  
P. Cooper
Keyword(s):  
Real Time ◽  
Community Hospital ◽  
Validation Cohort ◽  
Data Extraction ◽  
Characteristic Curve ◽  
Hospital Setting ◽  
Data Repository ◽  
Derivation Cohort ◽  
Effective Interventions ◽  
Patients At Risk

SummaryBackground: The Centers for Medicare and Medicaid Services’ Readmissions Reduction Program adjusts payments to hospitals based on 30-day readmission rates for patients with acute myocardial infarction, heart failure, and pneumonia. This holds hospitals accountable for a complex phenomenon about which there is little evidence regarding effective interventions. Further study may benefit from a method for efficiently and inexpensively identifying patients at risk of readmission. Several models have been developed to assess this risk, many of which may not translate to a U.S. community hospital setting.Objective: To develop a real-time, automated tool to stratify risk of 30-day readmission at a semi-rural community hospital.Methods: A derivation cohort was created by extracting demographic and clinical variables from the data repository for adult discharges from calendar year 2010. Multivariate logistic regression identified variables that were significantly associated with 30-day hospital readmission. Those variables were incorporated into a formula to produce a Risk of Readmission Score (RRS). A validation cohort from 2011 assessed the predictive value of the RRS. A SQL stored procedure was created to calculate the RRS for any patient and publish its value, along with an estimate of readmission risk and other factors, to a secure intranet site.Results: Eleven variables were significantly associated with readmission in the multivariate analysis of each cohort. The RRS had an area under the receiver operating characteristic curve (c-statistic) of 0.74 (95% CI 0.73-0.75) in the derivation cohort and 0.70 (95% CI 0.69-0.71) in the validation cohort.Conclusion: Clinical and administrative data available in a typical community hospital database can be used to create a validated, predictive scoring system that automatically assigns a probability of 30-day readmission to hospitalized patients. This does not require manual data extraction or manipulation and uses commonly available systems. Additional study is needed to refine and confirm the findings.Citation: Gildersleeve R, Cooper P. Development of an automated, real time surveillance tool for predicting readmissions at a community hospital. Appl Clin Inf 2013; 4: 153–169http://dx.doi.org/10.4338/ACI-2012-12-RA-0058

Application of Transthoracic Shear-Wave Ultrasound Elastography in Lung Lesions

2020 ◽  
pp. 2002347
Author(s):  
Yao-Wen Kuo ◽  
Yen-Lin Chen ◽  
Huey-Don Wu ◽  
Ying-Chun Chien ◽  
Chun-Kai Huang ◽  
...  
Keyword(s):  
Shear Wave ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Ultrasound Elastography ◽  
Youden Index ◽  
Lung Lesions ◽  
Derivation Cohort ◽  
Lung Malignancy ◽  
High Elasticity ◽  
Malignant Lesions

IntroductionThe tissue stiffness information may help in the diagnosis of lung lesions. This study aimed to investigate and validate the application of transthoracic two-dimensional shear-wave ultrasound elastography in differentiating malignant from benign subpleural lung lesions.MethodsThis study involved one retrospective observational derivation cohort from January 2016 to December 2017 and one prospective observational validation cohort from December 2017 to December 2019. The inclusion criterion was radiographic evidence of pulmonary lesions. The patients were categorised into the air-bronchogram and hypoechoic groups based on the B-mode grayscale images. The elasticity of subpleural lung lesions with acceptable shear-wave propagation was measured. Diagnoses were made on the basis of pathology, microbiological studies, or following up the clinical course for at least 6 months.ResultsA total of 354 patients were included. Among the 121 patients in the derivation cohort, a receiver operating characteristic curve was constructed and the cut-off point to differentiate benign from malignant lesions was 65 kPa with Youden index 0.60 and accuracy 84.3%. Among the 233 patients in the validation cohort, the diagnostic performance was maintained with Youden index 0.65 and accuracy 86.7%. Upon applying the cut-off point to the air-bronchogram group, Youden index was 0.70 and accuracy 85.0%.ConclusionsThis study validated the application of transthoracic shear-wave ultrasound elastography for assessing lung malignancy. A cut-off point of 65 kPa is suggested for predicting lung malignancy. Furthermore, for pulmonary air-bronchogram lesions with high elasticity, tissue proofing should be considered because of the high possibility of malignancy.

P2881Eligibility for a subcutaneous defibrillator based on standard 12-lead electrocardiogram

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
R Sakhi ◽  
D A M J Theuns ◽  
D Cosgun ◽  
M Michels ◽  
A F L Schinkel ◽  
...  
Keyword(s):  
At Risk ◽  
Validation Cohort ◽  
High Specificity ◽  
Implantable Defibrillator ◽  
P Value ◽  
Derivation Cohort ◽  
T Wave ◽  
Wave Inversion ◽  
Passing Rate ◽  
Patients At Risk

Abstract Background Currently, the eligibility for a subcutaneous implantable defibrillator (S-ICD) system relies on a pre-implant vector screening based on the automated screening tool (AST). Objective To determine 12-lead ECG characteristics associated with eligibility for an S-ICD in a heterogeneous population at risk for sudden cardiac death (SCD). The goal is to determine patient eligibility for S-ICD using the standard 12-lead ECG, thereby avoiding additional AST screening. Methods We prospectively evaluated the eligibility for an S-ICD in 254 consecutive patients at risk for SCD. We identified 12-lead ECG parameters which were independently associated with AST passing (≥1 vector) using multivariable logistical regression analysis in our derivation cohort. The final model was tested in a separate validation cohort. Results The overall passing rate was 92% in our derivation cohort. Independent 12-lead ECG characteristics associated with AST passing were QRS≤130 ms, absence of QRS/T discordance in lead II and R/T-ratio ≥3.5 in lead II (Table). Eighty-three of 254 patients (33%) fulfilled these three criteria and had a passing rate of 100%. Of the validation cohort, 37 of 60 patients (62%) fulfilled all three criteria and also had a passing rate of 100%. The interobserver agreement for applying the ECG model was 90% (Cohen's Kappa=0.80). Table 1 Variables Univariable Multivariable OR (95% CI) P-value OR (95% CI) P-value QRS ≤130 ms 9.65 (3.66–25.43) <0.01 8.09 (2.88–22.77) <0.01 QTc ≤450 ms 3.33 (1.18–9.54) 0.02 Absence of T-wave inversion in lead I 2.74 (1.03–7.25) 0.04 Absence of T-wave inversion in lead II 3.65 (1.29–10.33) 0.02 Absence of QRS/T-wave discordance in lead II 5.05 (1.98–12.92) <0.01 4.19 (1.49–11.74) <0.01 Absence of QRS/T-wave discordance in lead aVF 3.95 (1.53–10.19) <0.01 R/T-ratio ≥3.5 in lead II 3.58 (1.27–10.01) 0.02 4.21 (1.27–13.95) 0.02 R/T-ratio ≥3.5 in lead aVF 3.16 (1.18–8.42) 0.02 OR = odds ratio; CI = confidence interval. Figure 1 Conclusion Using the standard 12-lead ECG, we developed a simple screening model with a high specificity for S-ICD eligibility. Our results suggest that patients who fulfill the three ECG criteria do not need additional AST-screening. Therefore, we developed a simple flowchart to determine eligibility for an S-ICD that can be easily implemented in daily clinical practice (Figure).

scholarly journals An Innovative Method to Extract Data in a Real-time Data Warehousing Environment

2021 ◽  
Author(s):  
Flavio de Assis Vilela ◽  
Ricardo Rodrigues Ciferri
Keyword(s):  
Real Time ◽  
Data Warehousing ◽  
Data Extraction ◽  
Synthetic Data ◽  
Knowledge Discovery In Databases ◽  
Data Repository ◽  
Time Data ◽  
Innovative Method ◽  
Real Time Data ◽  
Time Requirements

ETL (Extract, Transform, and Load) is an essential process required to perform data extraction in knowledge discovery in databases and in data warehousing environments. The ETL process aims to gather data that is available from operational sources, process and store them into an integrated data repository. Also, the ETL process can be performed in a real-time data warehousing environment and store data into a data warehouse. This paper presents a new and innovative method named Data Extraction Magnet (DEM) to perform the extraction phase of ETL process in a real-time data warehousing environment based on non-intrusive, tag and parallelism concepts. DEM has been validated on a dairy farming domain using synthetic data. The results showed a great performance gain in comparison to the traditional trigger technique and the attendance of real-time requirements.

scholarly journals Towards Prevention of Acute Syndromes

2014 ◽  
Vol 05 (01) ◽  
pp. 58-72 ◽  
Author(s):  
C. Thongprayoon ◽  
B.W. Pickering ◽  
A. Akhoundi ◽  
G. Wilson ◽  
D. Pieczkiewicz ◽  
...  
Keyword(s):  
At Risk ◽  
Acute Pancreatitis ◽  
Hospital Admission ◽  
Data Extraction ◽  
Secondary Analysis ◽  
Reference Standard ◽  
Predictive Values ◽  
Derivation Cohort ◽  
Automated Algorithm ◽  
Patients At Risk

SummaryBackground: Identifying patients at risk for acute respiratory distress syndrome (ARDS) before their admission to intensive care is crucial to prevention and treatment. The objective of this study is to determine the performance of an automated algorithm for identifying selected ARDS predis-posing conditions at the time of hospital admission.Methods: This secondary analysis of a prospective cohort study included 3,005 patients admitted to hospital between January 1 and December 31, 2010. The automated algorithm for five ARDS pre-disposing conditions (sepsis, pneumonia, aspiration, acute pancreatitis, and shock) was developed through a series of queries applied to institutional electronic medical record databases. The automated algorithm was derived and refined in a derivation cohort of 1,562 patients and subsequently validated in an independent cohort of 1,443 patients. The sensitivity, specificity, and positive and negative predictive values of an automated algorithm to identify ARDS risk factors were compared with another two independent data extraction strategies, including manual data extraction and ICD-9 code search. The reference standard was defined as the agreement between the ICD-9 code, automated and manual data extraction.Results: Compared to the reference standard, the automated algorithm had higher sensitivity than manual data extraction for identifying a case of sepsis (95% vs. 56%), aspiration (63% vs. 42%), acute pancreatitis (100% vs. 70%), pneumonia (93% vs. 62%) and shock (77% vs. 41%) with similar specificity except for sepsis and pneumonia (90% vs. 98% for sepsis and 95% vs. 99% for pneumonia). The PPV for identifying these five acute conditions using the automated algorithm ranged from 65% for pneumonia to 91 % for acute pancreatitis, whereas the NPV for the automated algorithm ranged from 99% to 100%.Conclusion: A rule-based electronic data extraction can reliably and accurately identify patients at risk of ARDS at the time of hospital admission.Citation: Ahmed A, Thongprayoon C, Pickering BW, Akhoundi A, Wilson G, Pieczkiewicz D, Herasevich V. Towards prevention of acute syndromes: Electronic identification of at-risk patients during hospital admission. Appl Clin Inf 2014; 5: 58–72http://dx.doi.org/10.4338/ACI-2013-07-RA-0045

Predicting in-hospital mortality for patients admitted from the emergency department: An initial alarm score for inpatient care

2020 ◽  
Author(s):  
Ming-Ju Hsieh ◽  
Nin-Chieh Hsu ◽  
Yu-Feng Lin ◽  
Chin-Chung Shu ◽  
Wen-Chu Chiang ◽  
...  
Keyword(s):  
Emergency Department ◽  
Hospital Mortality ◽  
Predictive Value ◽  
Cancer Metastasis ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
High Sensitivity ◽  
Cutoff Value ◽  
Derivation Cohort ◽  
Cox Proportional Hazard Regression

Abstract Background: The in-hospital mortality of patients admitted from the emergency department (ED) is high, but no appropriate initial alarm score is available. Methods: This prospective observational study enrolled ED-admitted patients in hospitalist-care wards and analyzed the predictors for seven-day in-hospital mortality from May 2010 to October 2016. Two-thirds were randomly assigned to a derivation cohort for development of the model and cross-validation was performed in the validation cohort. Results: During the study period, 8,649 patients were enrolled for analysis. The mean age was 71.05 years, and 51.91% were male. The most common admission diagnoses were pneumonia (36%) and urinary tract infection (20.05%). In the derivation cohort, multivariable Cox proportional hazard regression revealed that a low Barthel index score, triage level 1 at the ED, presence of cancer, metastasis, and admission diagnoses of pneumonia and sepsis were independently associated with seven-day in-hospital mortality. Based on the probability developed from the multivariable model, the area under the receiver operating characteristic curve in the derivation group was 0.81 [0.79–0.85]. The result in the validation cohort was comparable. The prediction score modified by the six independent factors had high sensitivity of 88.03% and a negative predictive value of 99.51% for a cutoff value of 4, whereas the specificity and positive predictive value were 89.61% and 10.55%, respectively, when the cutoff value was a score of 6. Conclusion: The seven-day in-hospital mortality in a hospitalist-care ward is 2.8%. The initial alarm score could help clinicians to prioritize or exclude patients who need urgent and intensive care.

scholarly journals Simple Scoring Tool to Estimate Risk of Hospitalization and Mortality in Ambulatory and Emergency Department Patients with COVID-19

2021 ◽  
Author(s):  
Brandon J. Webb ◽  
Nicholas M. Levin ◽  
Nancy Grisel ◽  
Samuel M. Brown ◽  
Ithan D. Peltan ◽  
...  
Keyword(s):  
Monoclonal Antibodies ◽  
High Risk ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
External Validation ◽  
Receiver Operator Characteristic Curve ◽  
Multivariable Logistic Regression Model ◽  
White Race ◽  
Derivation Cohort ◽  
Scoring Tool

AbstractBackgroundAccurate methods of identifying patients with COVID-19 who are at high risk of poor outcomes has become especially important with the advent of limited-availability therapies such as monoclonal antibodies. Here we describe development and validation of a simple but accurate scoring tool to classify risk of hospitalization and mortality.MethodsAll consecutive patients testing positive for SARS-CoV-2 from March 25-October 1, 2020 within the Intermountain Healthcare system were included. The cohort was randomly divided into 70% derivation and 30% validation cohorts. A multivariable logistic regression model was fitted for 14-day hospitalization. The optimal model was then adapted to a simple, probabilistic score and applied to the validation cohort and evaluated for prediction of hospitalization and 28-day mortality.Results22,816 patients were included; mean age was 40 years, 50.1% were female and 44% identified as non-white race or Hispanic/Latinx ethnicity. 6.2% required hospitalization and 0.4% died. Criteria in the simple model included: age (0.5 points per decade); high-risk comorbidities (2 points each): diabetes mellitus, severe immunocompromised status and obesity (body mass index≥30); non-white race/Hispanic or Latinx ethnicity (2 points), and 1 point each for: male sex, dyspnea, hypertension, coronary artery disease, cardiac arrythmia, congestive heart failure, chronic kidney disease, chronic pulmonary disease, chronic liver disease, cerebrovascular disease, and chronic neurologic disease. In the derivation cohort (n=16,030) area under the receiver-operator characteristic curve (AUROC) was 0.82 (95% CI 0.81-0.84) for hospitalization and 0.91 (0.83-0.94) for 28-day mortality; in the validation cohort (n=6,786) AUROC for hospitalization was 0.8 (CI 0.78-0.82) and for mortality 0.8 (CI 0.69-0.9).ConclusionA prediction score based on widely available patient attributes accurately risk stratifies patients with COVID-19 at the time of testing. Applications include patient selection for therapies targeted at preventing disease progression in non-hospitalized patients, including monoclonal antibodies. External validation in independent healthcare environments is needed.

scholarly journals Overview and Strategy Analysis of Technology-based Non-pharmacological Interventions for Delirium Prevention: Systematic Review (Preprint)

2020 ◽  
Author(s):  
Chanmi Kim ◽  
Esther M. van der Heide ◽  
Thomas J. L. van Rompay ◽  
Gijsbertus J. Verkerke ◽  
Geke. D. S. Ludden
Keyword(s):  
Systematic Review ◽  
Data Extraction ◽  
Hospital Setting ◽  
Psychological Needs ◽  
The Body ◽  
Strategy Analysis ◽  
Positive Effects ◽  
Effective Interventions ◽  
Systematic Effort ◽  
Delirium Prevention

BACKGROUND Delirium prevention is crucial, especially in critically ill patients. Increasingly, non-pharmacological multicomponent interventions for preventing delirium are recommended and technology-based interventions have developed to support them. Despite the increasing number and diversity in technology-based interventions, there has been no systematic effort to create an overview. OBJECTIVE The systematic review was carried out to answer the following questions: (1) What are technologies currently used in non-pharmacological technology-based interventions for preventing and reducing delirium?, (2) What are the strategies underlying these currently used technologies? METHODS A systematic search was conducted in Scopus and Embase between 2015 and 2020. A selection was made following the PRISMA guideline. Studies were eligible if they contained any types of technology-based interventions and assessed delirium-/risk factor-related outcome measures in a hospital setting. Data extraction and quality assessment were performed using a predesigned data form. RESULTS A total of 31 studies were included and analyzed focusing on the types of technology and the strategies used in the interventions. The review revealed eight different technology types and 14 strategies that were categorized into seven pathways: (1) restore circadian rhythm, (2) activate the body, (3) activate the mind, (4) induce relaxation, provide (5) a sense of security, (6) a sense of control, and (7) a sense of being connected. For all technology types, significant positive effects were found on direct and/or indirect delirium outcome. Several similarities were found across effective interventions: using a multicomponent approach and/or including components comforting psychological needs of patients (e.g., familiarity, distraction and soothing elements). CONCLUSIONS Technology-based interventions have a high potential when multidimensional needs of patients (e.g., physical, cognitive and emotional) are incorporated. The seven pathways pinpoint starting points for building more effective technology-based interventions. Opportunities were discussed for transforming the Intensive Care Unit (ICU) into a healing environment as a powerful tool to prevent delirium.

scholarly journals Development of a brief scoring system to predict any-cause mortality in patients hospitalized with COVID-19 infection

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254580
Author(s):  
Nasheena Jiwa ◽  
Rahul Mutneja ◽  
Lucie Henry ◽  
Garrett Fiscus ◽  
Richard Zu Wallack
Keyword(s):  
Hospital Mortality ◽  
Random Sample ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Scoring Systems ◽  
Supplemental Oxygen ◽  
Derivation Cohort ◽  
Reactive Protein ◽  
Time Period ◽  
Robust Separation

Patients hospitalized with COVID-19 infection are at a high general risk for in-hospital mortality. A simple and easy-to-use model for predicting mortality based on data readily available to clinicians in the first 24 hours of hospital admission might be useful in directing scarce medical and personnel resources toward those patients at greater risk of dying. With this goal in mind, we evaluated factors predictive of in-hospital mortality in a random sample of 100 patients (derivation cohort) hospitalized for COVID-19 at our institution in April and May, 2020 and created potential models to test in a second random sample of 148 patients (validation cohort) hospitalized for the same disease over the same time period in the same institution. Two models (Model A: two variables, presence of pneumonia and ischemia); (Model B: three variables, age > 65 years, supplemental oxygen ≥ 4 L/min, and C-reactive protein (CRP) > 10 mg/L) were selected and tested in the validation cohort. Model B appeared the better of the two, with an AUC in receiver operating characteristic curve analysis of 0.74 versus 0.65 in Model A, but the AUC differences were not significant (p = 0.24. Model B also appeared to have a more robust separation of mortality between the lowest (none of the three variables present) and highest (all three variables present) scores at 0% and 71%, respectively. These brief scoring systems may prove to be useful to clinicians in assigning mortality risk in hospitalized patients.

Abstract TMP85: Predictive Score of Hemorrhagic Transformation in Patients Not Submitted to Reperfusion Therapies - PROpHET

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Joao B Andrade ◽  
Gisele S Silva ◽  
Jay P Mohr ◽  
Joao J Carvalho ◽  
Luisa Franciscatto ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Reperfusion Therapy ◽  
Predictive Score ◽  
Brier Score ◽  
Multivariable Logistic Regression Model ◽  
Derivation Cohort ◽  
Digital Version

Objective: To create an accurate and user-friendly pr edictive sc o re for he morrhagic t ransformation in patients not submitted to reperfusion therapies (PROpHET). Methods: We created a multivariable logistic regression model to assess the prediction of Hemorrhage Transformation (HT) for acute ischemic strokes not treated with reperfusion therapy. One point was assigned for each of gender, cardio-aortic embolism, hyperdense middle cerebral artery sign, leukoaraiosis, hyperglycemia, 2 points for ASPECTS ≤7, and -3 points for lacunar syndrome. Acute ischemic stroke patients admitted to the Fortaleza Comprehensive Stroke Center in Brazil from 2015 to 2017 were randomly selected to the derivation cohort. The validation cohort included similar, but not randomized, cases from 5 Brazilian and one American Comprehensive Stroke Centers. Symptomatic cases were defined as NIHSS ≥4 at 24 hours after the event. Results from the derivation and validation cohorts were assessed with the area under the receiver operating characteristic curve (AUC-ROC). Results: From 2,432 of acute ischemic stroke screened in Fortaleza, 448 were prospectively selected for the derivation cohort and a 7-day follow-up. From 1,847 not selected, 577 underwent reperfusion therapy, 734 were excluded due to inadequate imaging or refusal of consent, and 538 whose data were obtained retrospectively and were selected only for the validation cohort. A score ≥3 had 78% sensitivity and 75% specificity, AUC-ROC 0.82 for all cases of HT, Hosmer-Lemeshow 0.85, Brier Score 0.1, and AUC-ROC 0.83 for those with symptomatic HT. An AUC-ROC of 0.84 was found for the validation cohort of 1,910 from all 6 centers, and a score ≥3 was found in 65% of patients with HT against 11.3% of those without HT. In comparison with 8 published predictive scores of HT, PROpHET was the most accurate (p < 0.01). Conclusions: PROpHET offers a tool simple, quick and easy-to-perform to estimate risk stratification of HT in patients not submitted to RT. A digital version of PROpHET is available in www.score-prophet.com Classification of evidence: This study provides Class I evidence from prospective data acquisition.

scholarly journals Risk Score for Predicting In-Hospital Mortality in COVID-19 (RIM Score)

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 596 ◽  
Author(s):  
Alejandro López-Escobar ◽  
Rodrigo Madurga ◽  
José María Castellano ◽  
Sara Velázquez ◽  
Rafael Suárez del Villar ◽  
...  
Keyword(s):  
At Risk ◽  
Hospital Mortality ◽  
Validation Cohort ◽  
Health Care Systems ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Oxygen Saturation Level ◽  
Training Cohort ◽  
Care Systems ◽  
Patients At Risk

Infection by SARS-CoV2 has devastating consequences on health care systems. It is a global health priority to identify patients at risk of fatal outcomes. 1955 patients admitted to HM-Hospitales from 1 March to 10 June 2020 due to COVID-19, were were divided into two groups, 1310 belonged to the training cohort and 645 to validation cohort. Four different models were generated to predict in-hospital mortality. Following variables were included: age, sex, oxygen saturation, level of C-reactive-protein, neutrophil-to-platelet-ratio (NPR), neutrophil-to-lymphocyte-ratio (NLR) and the rate of changes of both hemogram ratios (VNLR and VNPR) during the first week after admission. The accuracy of the models in predicting in-hospital mortality were evaluated using the area under the receiver-operator-characteristic curve (AUC). AUC for models including NLR and NPR performed similarly in both cohorts: NLR 0.873 (95% CI: 0.849–0.898), NPR 0.875 (95% CI: 0.851–0.899) in training cohort and NLR 0.856 (95% CI: 0.818–0.895), NPR 0.863 (95% CI: 0.826–0.901) in validation cohort. AUC was 0.885 (95% CI: 0.885–0.919) for VNLR and 0.891 (95% CI: 0.861–0.922) for VNPR in the validation cohort. According to our results, models are useful in predicting in-hospital mortality risk due to COVID-19. The RIM Score proposed is a simple, widely available tool that can help identify patients at risk of fatal outcomes.

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