Dataset Information

Distinguishing Coronavirus Disease 2019 Patients From General Surgery Emergency Patients With the CIAAD Scale: Development and Validation of a Prediction Model Based on 822 Cases in China.

ABSTRACT: Background: During the epidemic, surgeons cannot identify infectious acute abdomen patients with suspected coronavirus disease 2019 (COVID-19) immediately using the current widely applied methods, such as double nucleic acid detection. We aimed to develop and validate a prediction model, presented as a nomogram and scale, to identify infectious acute abdomen patients with suspected COVID-19 more effectively and efficiently. Methods: A total of 584 COVID-19 patients and 238 infectious acute abdomen patients were enrolled. The least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analyses were conducted to develop the prediction model. The performance of the nomogram was evaluated through calibration curves, Receiver Operating Characteristic (ROC) curves, decision curve analysis (DCA), and clinical impact curves in the training and validation cohorts. A simplified screening scale and a management algorithm were generated based on the nomogram. Results: Five potential COVID-19 prediction variables, fever, chest CT, WBC, CRP, and PCT, were selected, all independent predictors of multivariable logistic regression analysis, and the nomogram, named the COVID-19 Infectious Acute Abdomen Distinguishment (CIAAD) nomogram, was generated. The CIAAD nomogram showed good discrimination and calibration, and it was validated in the validation cohort. Decision curve analysis revealed that the CIAAD nomogram was clinically useful. The nomogram was further simplified as the CIAAD scale. Conclusion: We established an easy and effective screening model and scale for surgeons in the emergency department to use to distinguish COVID-19 patients. The algorithm based on the CIAAD scale will help surgeons more efficiently manage infectious acute abdomen patients suspected of having COVID-19.

SUBMITTER: Zhao B

PROVIDER: S-EPMC8119634 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:IntroductionTransferred emergency general surgery (EGS) patients are a vulnerable, high acuity population. The outcomes of and health care utilization among transferred (TRAN) as compared to directly admitted (DA) patients have been studied primarily using single institution or hospital system data which limits generalizability. We evaluated these outcomes among EGS patients using a national database.MethodsWe identified encounters of patients aged ≥18 years with a diagnosis of EGS as defined by the American Association for the Surgery of Trauma in the 2008-2011 Nationwide Inpatient Sample (NIS). Multivariable regression analyses determined if transfer status independently predicted in-hospital mortality (logistic regression) and morbidity (presence of any complication among those who survived to discharge; logistic regression), cost (log-linear regression), and duration of stay (among those who survived to discharge; log-linear regression) accounting for the NIS sampling design.ResultsWe identified 274,145 TRAN (57,885 unweighted) and 10,456,100 DA (2,187,132 unweighted) encounters. On univariate analysis, TRAN patients were more likely to have greater comorbidity scores, have Medicare insurance, and reside in an area with a lesser median household income compared to DA patients (p<0.0001). Mortality was greater in the TRAN vs DA groups (4.4% vs 1.6%; p<0.0001). Morbidity (presence of any complication) was also greater among TRAN patients (38.8% vs 26.1%; p<0.0001). Morbidity among TRAN patients was primarily due to urinary- (13.7%), gastrointestinal- (12.9%), and pulmonary-related (13.3%) complications. Median duration of hospital stay was 4.3 days for TRAN vs 3.0 days for DA (p<0.0001) patients. Median cost was greater for TRAN patients ($8,935 vs $7,167; p<0.0001). Regression analyses determined that after adjustment, TRAN patients had statistically significantly greater mortality, morbidity, and cost as well as longer durations of stay.ConclusionsEGS patients who are transferred experience increased in-hospital morbidity and mortality as well as increased durations of stay and cost. As the population and age of patients diagnosed with EGS conditions increase while the EGS workforce decreases, the need for inter-hospital transfers will increase. Identifying risk factors associated with worse outcomes among transferred patients can inform the design of initiatives in performance improvement and direct the finite resources available to this vulnerable patient population.

Project description:BackgroundAlthough nearly 1 million older adults are admitted for emergency general surgery conditions yearly, the extent to which baseline health influences the development and treatment of emergency general surgery conditions is unknown. We evaluated baseline health and older patients with and without emergency general surgery conditions.MethodsWe used the prospectively collected Medicare Current Beneficiary Survey with Medicare claims and 2 validated health frameworks: (1) Deficit Accumulation Frailty Score and (2) Complex Multimorbidity. Self-reported health and function items were used to derive pre-emergency general surgery conditions Deficit Accumulation Frailty Score and Complex Multimorbidity scores. Deficit Accumulation Frailty Score ranges from 0 (no frailty deficits) to 100 (all possible deficits present). Complex Multimorbidity is a 3-point categorical rank based on the presence of chronic conditions, functional limitations, and geriatric syndromes. Specific survey factors were also examined to determine association with development of emergency general surgery conditions or use of operative management.ResultsOf 54,417 individuals, 1,960 had emergency general surgery conditions (median age 79 [interquartile range 73-84]). Patients with emergency general surgery conditions had significantly higher Deficit Accumulation Frailty Score (19 [interquartile range 11-31] vs 14 [8-24]) and were more likely to be in the most severe Complex Multimorbidity category (38% vs 29%). Emergency general surgery conditions patients had higher proportions of nearly every health category, with the most striking differences in functional limitations. Patients who were treated nonoperatively had the poorest overall baseline health.ConclusionPatients who developed emergency general surgery conditions had more severe health burden than patients who did not, particularly in functional status. Clinicians must better understand the interaction between baseline health vulnerability and emergency surgical disease to improve prognostication and ensure alignment of patient goals and treatment strategies.

Project description:In late December 2019, patients of atypical pneumonia due to an unidentified microbial agent were reported in Wuhan, Hubei Province, China. Subsequently, a novel coronavirus was identified as the causative pathogen which was named SARS-CoV-2. As of 12 February 2020, more than 44 000 cases of SARS-CoV-2 infection have been confirmed in China and continue to expand. Provinces, municipalities and autonomous regions of China have launched first-level response to major public health emergencies one after another from 23 January 2020, which means restricting movement of people among provinces, municipalities and autonomous regions. The aim of this study was to explore the correlation between the migration scale index and the number of confirmed coronavirus disease 2019 (COVID-19) cases and to depict the effect of restricting population movement. In this study, Excel 2010 was used to demonstrate the temporal distribution at the day level and SPSS 23.0 was used to analyse the correlation between the migration scale index and the number of confirmed COVID-19 cases. We found that since 23 January 2020, Wuhan migration scale index has dropped significantly and since 26 January 2020, Hubei province migration scale index has dropped significantly. New confirmed COVID-19 cases per day in China except for Wuhan gradually increased since 24 January 2020, and showed a downward trend from 6 February 2020. New confirmed COVID-19 cases per day in China except for Hubei province gradually increased since 24 January 2020, and maintained at a high level from 24 January 2020 to 4 February 2020, then showed a downward trend. Wuhan migration scale index from 9 January to 22 January, 10 January to 23 January and 11 January to 24 January was correlated with the number of new confirmed COVID-19 cases per day in China except for Wuhan from 22 January to 4 February. Hubei province migration scale index from 10 January to 23 January and 11 January to 24 January was correlated with the number of new confirmed COVID-19 cases per day in China except for Hubei province from 22 January to 4 February. Our findings suggested that people who left Wuhan from 9 January to 22 January, and those who left Hubei province from 10 January to 24 January, led to the outbreak in the rest of China. The 'Wuhan lockdown' and the launching of the first-level response to this major public health emergency may have had a good effect on controlling the COVID-19 epidemic. Although new COVID-19 cases continued to be confirmed in China outside Wuhan and Hubei provinces, in our opinion, these are second-generation cases.

Project description:ImportanceCoronavirus disease 2019 patients have an increased risk of thrombotic complications that may reflect immunothrombosis, a process characterized by blood clotting, endothelial dysfunction, and the release of neutrophil extracellular traps. To date, few studies have investigated longitudinal changes in immunothrombosis biomarkers in these patients. Furthermore, how these longitudinal changes differ between coronavirus disease 2019 patients and noncoronavirus disease septic patients with pneumonia are unknown.ObjectivesIn this pilot observational study, we investigated the utility of immunothrombosis biomarkers for distinguishing between coronavirus disease 2019 patients and noncoronavirus disease septic patients with pneumonia. We also evaluated the utility of the biomarkers for predicting ICU mortality in these patients.Design setting and participantsThe participants were ICU patients with coronavirus disease 2019 (n = 14), noncoronavirus disease septic patients with pneumonia (n = 19), and healthy age-matched controls (n = 14).Main outcomes and measuresNine biomarkers were measured from plasma samples (on days 1, 2, 4, 7, 10, and/or 14). Analysis was based on binomial logit models and receiver operating characteristic analyses.ResultsCell-free DNA, d-dimer, soluble endothelial protein C receptor, protein C, soluble thrombomodulin, fibrinogen, citrullinated histones, and thrombin-antithrombin complexes have significant powers for distinguishing coronavirus disease 2019 patients from healthy individuals. In comparison, fibrinogen, soluble endothelial protein C receptor, antithrombin, and cell-free DNA have significant powers for distinguishing coronavirus disease 2019 from pneumonia patients. The predictors of ICU mortality differ between the two patient groups: soluble thrombomodulin and citrullinated histones for coronavirus disease 2019 patients, and protein C and cell-free DNA or fibrinogen for pneumonia patients. In both patient groups, the most recent biomarker values have stronger prognostic value than their ICU day 1 values.Conclusions and relevanceFibrinogen, soluble endothelial protein C receptor, antithrombin, and cell-free DNA have utility for distinguishing coronavirus disease 2019 patients from noncoronavirus disease septic patients with pneumonia. The most important predictors of ICU mortality are soluble thrombomodulin/citrullinated histones for coronavirus disease 2019 patients, and protein C/cell-free DNA for noncoronavirus disease pneumonia patients. This hypothesis-generating study suggests that the pathophysiology of immunothrombosis differs between the two patient groups.

Dataset Information

Distinguishing Coronavirus Disease 2019 Patients From General Surgery Emergency Patients With the CIAAD Scale: Development and Validation of a Prediction Model Based on 822 Cases in China.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets