New Scoring System for Predicting Mortality in Patients with COVID-19.
Ontology highlight
ABSTRACT: Purpose
We aimed to develop a novel mortality scoring system for inpatients with COVID-19 based on simple demographic factors and laboratory findings.Materials and methods
We reviewed and analyzed data from patients who were admitted and diagnosed with COVID-19 at 10 hospitals in Daegu, South Korea, between January and July 2020. We randomized and assigned patients to the development and validation groups at a 70% to 30% ratio. Each point scored for selected risk factors helped build a new mortality scoring system using Cox regression analysis. We evaluated the accuracy of the new scoring system in the development and validation groups using the area under the curve.Results
The development group included 1232 patients, whereas the validation group included 528 patients. In the development group, predictors for the new scoring system as selected by Cox proportional hazards model were age ≥70 years, diabetes, chronic kidney disease, dementia, C-reactive protein levels >4 mg/dL, infiltration on chest X-rays at the initial diagnosis, and the need for oxygen support on admission. The areas under the curve for the development and validation groups were 0.914 [95% confidence interval (CI) 0.891-0.937] and 0.898 (95% CI 0.854-0.941), respectively. According to our scoring system, COVID-19 mortality was 0.4% for the low-risk group (score 0-3) and 53.7% for the very high-risk group (score ≥11).Conclusion
We developed a new scoring system for quickly and easily predicting COVID-19 mortality using simple predictors. This scoring system can help physicians provide the proper therapy and strategy for each patient.
SUBMITTER: Bae S
PROVIDER: S-EPMC8382723 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundCoronavirus disease 2019 (COVID-19) has been widely spread and caused tens of thousands of deaths, especially in patients with severe COVID-19. This analysis aimed to explore risk factors for mortality of severe COVID-19, and establish a scoring system to predict in-hospital deaths.MethodsPatients with COVID-19 were retrospectively analyzed and clinical characteristics were compared. LASSO regression as well as multivariable analysis were used to screen variables and establish prediction model.FindingsA total of 2529 patients with COVID-19 was retrospectively analyzed, and 452 eligible severe COVID-19 were used for finally analysis. In training cohort, the median age was 66•0 years while it was 73•0 years in non-survivors. Patients aged 60-75 years accounted for the largest proportion of infected populations and mortality toll. Anti-SARS-CoV-2 antibodies were monitored up to 54 days, and IgG levels reached the highest during 20-30 days. No differences were observed of antibody levels between severe and non-severe patients. About 60.2% of severe patients had complications. Among acute myocardial injury (AMI), acute kidney injury (AKI) and acute liver injury (ALI), the heart was the earliest injured organ, whereas the time from AKI to death was the shortest. Age, diabetes, coronary heart disease (CHD), percentage of lymphocytes (LYM%), procalcitonin (PCT), serum urea, C reactive protein and D-dimer (DD), were identified associated with mortality by LASSO binary logistic regression. Then multivariable analysis was performed to conclude that old age, CHD, LYM%, PCT and DD remained independent risk factors for mortality. Based on the above variables, a scoring system of COVID-19 (CSS) was established to divide patients into low-risk and high-risk groups. This model displayed good discrimination (AUC=0·919) and calibration (P=0·264). Complications in low-risk and high-risk groups were significantly different (P<0·05). Use of corticosteroids in low-risk groups increased hospital stays by 4·5 days (P=0·036) and durations of disease by 7·5 days (P=0·012) compared with no corticosteroids.InterpretationOld age, CHD, LYM%, PCT and DD were independently related to mortality. CSS was useful for predicting in-hospital mortality and complications, and it could help clinicians to identify high-risk patients with poor prognosis.FundingThis work was supported by the Key Project for Anti-2019 novel Coronavirus Pneumonia from the Ministry of Science and Technology, China (grant number 2020YFC0845500).
| S-EPMC7332889 | biostudies-literature
Project description:BackgroundCorona Virus Disease 2019 (COVID-19) presentations range from those similar to the common flu to severe pneumonia resulting in hospitalization with significant morbidity and/or mortality. In this study, we made an attempt to develop a predictive scoring model to improve the early detection of high risk COVID-19 patients by analyzing the clinical features and laboratory data available on admission.MethodsWe retrospectively included 480 consecutive adult patients, aged 21-95, who were admitted to Faghihi Teaching Hospital. Clinical and laboratory features were collected from the medical records and analyzed using multiple logistic regression analysis. The final data analysis was utilized to develop a simple scoring model for the early prediction of mortality in COVID-19 patients. The score given to each associated factor was based on the coefficients of the regression analyses.ResultsA novel mortality risk score (COVID-19 BURDEN) was derived, incorporating risk factors identified in this cohort. CRP (> 73.1 mg/L), O2 saturation variation (greater than 90%, 84-90%, and less than 84%), increased PT (> 16.2 s), diastolic blood pressure (≤ 75 mmHg), BUN (> 23 mg/dL), and raised LDH (> 731 U/L) were the features constituting the scoring system. The patients are triaged to the groups of low- (score < 4) and high-risk (score ≥ 4) groups. The area under the curve, sensitivity, and specificity for predicting mortality in patients with a score of ≥ 4 were 0.831, 78.12%, and 70.95%, respectively.ConclusionsUsing this scoring system in COVID-19 patients, the patients with a higher risk of mortality can be identified which will help to reduce hospital care costs and improve its quality and outcome.
| S-EPMC9807969 | biostudies-literature
Project description:IntroductionCoronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 virus, is a major public health concern spanning from healthy carriers to patients with life-threatening conditions. Although most of COVID-19 patients have mild-to-moderate clinical symptoms, some patients have severe pneumonia leading to death. Therefore, the early prediction of disease prognosis and severity is crucial in COVID-19 patients. The main objective of this study is to evaluate the haemocytometric parameters and identify severity score associated with SARS-CoV-2 infection.MethodsClinical and laboratory records were retrospectively reviewed from 97 cases of COVID-19 admitted to hospitals in Istanbul, Turkey. The patient groups were subdivided into three major groups: Group 1 (Non-critical): 59 patients, Group 2 (Critical-Survivors): 23 patients and Group 3 (Critical-Non-survivors):15 patients. These data was tested for correlation, including with derived haemocytometric parameters. The blood analyses were performed the Sysmex XN-series automated hematology analyser using standard laboratory protocols. All statistical testing was undertaken using Analyse-it software.Results97 patients with COVID-19 disease and 935 sequential complete blood count (CBC-Diff) measurements (days 0-30) were included in the final analyses. Multivariate analysis demonstrated that red cell distribution width (RDW) (>13.7), neutrophil to lymphocyte ratio (NLR) (4.4), Hemoglobin (Hgb) (<11.4 gr/dL) and monocyte to neutrophil ratio (MNR) (0.084) had the highest area under curve (AUC) values, respectively in discrimination critical patients than non-critical patients. In determining Group 3, MNR (<0.095), NLR (>5.2), Plateletcount (PLT) (>142 x103/L) and RDW (>14) were important haemocytometric parameters, and the mortality risk value created by their combination had the highest AUC value (AUC = 0.911, 95% CI, 0886-0.931). Trend analysis of CBC-Diff parameters over 30 days of hospitalization, NLR on day 2, MNR on day 4, RDW on day 6 and PLT on day 7 of admission were found to be the best time related parameters in discrimination non-critical (mild-moderate) patient group from critical (severe and non-survivor) patient group.ConclusionNLR is a strong predictor for the prognosis for severe COVID-19 patients when the cut-off chosen was 4.4, the combined mortality risk factor COVID-19 disease generated from RDW-CV, NLR, MNR and PLT is best as a mortality haematocytometric index.
| S-EPMC8341498 | biostudies-literature
Project description: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.
| S-EPMC8284608 | biostudies-literature
Project description:SARS-CoV-2 virus has rapidly spread worldwide since December 2019, causing COVID-19 disease. In-hospital mortality is a common indicator for evaluating treatment outcomes. Therefore, the developing and validating a simple score system from observational data could assist in modulating the management procedures. A retrospective cohort study included all data records of patients with positive PCR for SARS-CoV-2. The factors that associated with mortality were analyzed, then allocation of potential predictors of mortality was executed using different logistic regression modeling, subsequently scoring system was developed from the most weighted predictors. The mortality rate of patients with COVID-19 pneumonia was 28.5% and 28.74%, respectively. The most significant factors that affected in-hospital mortality were old age (> 60 years), delay in hospital admission (> 4 days), high neutrophil/lymphocyte ratio "NLR" (> 3); higher computed tomography severity score; and CT-SS (> 20), in addition to using remdesivir and tocilizumab in the treatment protocol (P < 0.001 for all). The validity of the newly performed score was significant; the AUC was 85%, P < 0.001, and its prognostic utility was good; the AUC was 75%, P < 0.001. The prognostic utility of newly developed score system (EGY.Score) was excellent and could be used to adjust the treatment strategy of highly at-risk patients with COVID-19 pneumonia.
| S-EPMC9791155 | biostudies-literature
Project description:BackgroundAn accurate system to predict mortality in patients requiring intubation for COVID-19 could help to inform consent, frame family expectations and assist end-of-life decisions.Research objectiveTo develop and validate a mortality prediction system called C-TIME (COVID-19 Time of Intubation Mortality Evaluation) using variables available before intubation, determine its discriminant accuracy, and compare it to acute physiology and chronic health evaluation (APACHE IVa) and sequential organ failure assessment (SOFA).MethodsA retrospective cohort was set in 18 medical-surgical ICUs, enrolling consecutive adults, positive by SARS-CoV 2 RNA by reverse transcriptase polymerase chain reaction or positive rapid antigen test, and undergoing endotracheal intubation. All were followed until hospital discharge or death. The combined outcome was hospital mortality or terminal extubation with hospice discharge. Twenty-five clinical and laboratory variables available 48 hours prior to intubation were entered into multiple logistic regression (MLR) and the resulting model was used to predict mortality of validation cohort patients. Area under the receiver operating curve (AUROC) was calculated for C-TIME, APACHE IVa and SOFA.ResultsThe median age of the 2,440 study patients was 66 years; 61.6 percent were men, and 50.5 percent were Hispanic, Native American or African American. Age, gender, COPD, minimum mean arterial pressure, Glasgow Coma scale score, and PaO2/FiO2 ratio, maximum creatinine and bilirubin, receiving factor Xa inhibitors, days receiving non-invasive respiratory support and days receiving corticosteroids prior to intubation were significantly associated with the outcome variable. The validation cohort comprised 1,179 patients. C-TIME had the highest AUROC of 0.75 (95%CI 0.72-0.79), vs 0.67 (0.64-0.71) and 0.59 (0.55-0.62) for APACHE and SOFA, respectively (Chi2 P<0.0001).ConclusionsC-TIME is the only mortality prediction score specifically developed and validated for COVID-19 patients who require mechanical ventilation. It has acceptable discriminant accuracy and goodness-of-fit to assist decision-making just prior to intubation. The C-TIME mortality prediction calculator can be freely accessed on-line at https://phoenixmed.arizona.edu/ctime.
| S-EPMC9258832 | biostudies-literature
Project description:BackgroundDevelopment of a prediction model using baseline characteristics of tuberculosis (TB) patients at the time of diagnosis will aid us in early identification of the high-risk groups and devise pertinent strategies accordingly. Hence, we did this study to develop a prognostic-scoring model for predicting the death among newly diagnosed drug sensitive pulmonary TB patients in South India.MethodsWe undertook a longitudinal analysis of cohort data under the Regional Prospective Observational Research for Tuberculosis India consortium. Multivariable cox regression using the stepwise backward elimination procedure was used to select variables for the model building and the nomogram-scoring system was developed with the final selected model.ResultsIn total, 54 (4.6%) out of the 1181 patients had died during the 1-year follow-up period. The TB mortality rate was 0.20 per 1000 person-days. Eight variables (age, gender, functional limitation, anemia, leukopenia, thrombocytopenia, diabetes, neutrophil-lymphocyte ratio) were selected and a nomogram was built using these variables. The discriminatory power was 0.81 (95% confidence interval: 0.75-0.86) and this model was well-calibrated. Decision curve analysis showed that the model is beneficial at a threshold probability ~15-65%.ConclusionsThis scoring system could help the clinicians and policy makers to devise targeted interventions and in turn reduce the TB mortality in India.
| S-EPMC10273380 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2021-07-24 | GSE180743 | GEO
Project description:Introduction: Liver cirrhosis is caused by the development of various acute and chronic liver diseases. Esophageal varices is a common and serious complication of liver cirrhosis during decompensation. Despite the development of various treatments, the prognosis for liver cirrhosis with esophageal varices (LCEV) remains poor. We aimed to establish and validate a nomogram for predicting in-hospital death in LCEV patients. Methods: Data on LCEV patients were extracted from the Medical Information Mart for Intensive Care III and IV (MIMIC-III and MIMIC-IV) database. The patients from MIMIC-III were randomly divided into training and validation cohorts. Training cohort was used for establishing the model, validation and MIMIC-IV cohorts were used for validation. The independent prognostic factors for LCEV patients were determined using the least absolute shrinkage and selection operator (LASSO) method and forward stepwise logistic regression. We then constructed a nomogram to predict the in-hospital death of LCEV patients. Multiple indicators were used to validate the nomogram, including the area under the receiver operating characteristic curve (AUC), calibration curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification index (NRI), and decision curve analysis (DCA). Results: Nine independent prognostic factors were identified by using LASSO and stepwise regressions: age, Elixhauser score, anion gap, sodium, albumin, bilirubin, international normalized ratio, vasopressor use, and bleeding. The nomogram was then constructed and validated. The AUC value of the nomogram was 0.867 (95% CI = 0.832-0.904) in the training cohort, 0.846 (95% CI = 0.790-0.896) in the validation cohort and 0.840 (95% CI = 0.807-0.872) in the MIMIC-IV cohort. High AUC values indicated the good discriminative ability of the nomogram, while the calibration curves and the Hosmer-Lemeshow test results demonstrated that the nomogram was well-calibrated. Improvements in NRI and IDI values suggested that our nomogram was superior to MELD-Na, CAGIB, and OASIS scoring system. DCA curves indicated that the nomogram had good value in clinical applications. Conclusion: We have established the first prognostic nomogram for predicting the in-hospital death of LCEV patients. The nomogram is easy to use, performs well, and can be used to guide clinical practice, but further external prospective validation is still required.
| S-EPMC8542681 | biostudies-literature
Project description:Using structured elements from Electronic Health Records (EHR), we seek to: i ) build predictive models to stratify patients tested for COVID-19 by their likelihood for hospitalization, ICU admission, mechanical ventilation and inpatient mortality, and ii ) identify the most important EHR-based features driving the predictions. We leveraged EHR data from the Duke University Health System tested for COVID-19 or hospitalized between March 11, 2020 and August 24, 2020, to build models to predict hospital admissions within 4 weeks. Models were also created for ICU admissions, need for mechanical ventilation and mortality following admission. Models were developed on a cohort of 86,355 patients with 112,392 outpatient COVID-19 tests or any-cause hospital admissions between March 11, 2020 and June 4, 2020. The four models considered resulted in AUROC=0.838 (CI: 0.832-0.844) and AP=0.272 (CI: 0.260-0.287) for hospital admissions, AUROC=0.847 (CI: 0.839-855) and AP=0.585 (CI: 0.565-0.603) for ICU admissions, AUROC=0.858 (CI: 0.846-0.871) and AP=0.434 (CI: 0.403-0.467) for mechanical ventilation, and AUROC=0.0.856 (CI: 0.842-0.872) and AP=0.243 (CI: 0.205-0.282) for inpatient mortality. Patient history abstracted from the EHR has the potential for being used to stratify patients tested for COVID-19 in terms of utilization and mortality. The dominant EHR features for hospital admissions and inpatient outcomes are different. For the former, age, social indicators and previous utilization are the most important predictive features. For the latter, age and physiological summaries (pulse and blood pressure) are the main drivers.
| S-EPMC7743096 | biostudies-literature