scholarly journals Predicting Risk Score for Mechanical Ventilation in Hospitalized Adult Patients Suffering from COVID-19

2021 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Samira Kafan ◽  
Kiana Tadbir Vajargah ◽  
Mehrdad Sheikhvatan ◽  
Gholamreza Tabrizi ◽  
Ahmad Salimzadeh ◽  
...  

Background: COVID-19 has become a pandemic since December 2019, causing millions of deaths worldwide. It has a wide spectrum of severity, ranging from mild infection to severe illness requiring mechanical ventilation. In the middle of a pandemic, when medical resources (including mechanical ventilators) are scarce, there should be a scoring system to provide the clinicians with the information needed for clinical decision-making and resource allocation. Objectives: This study aimed to develop a scoring system based on the data obtained on admission, to predict the need for mechanical ventilation in COVID-19 patients. Methods: This study included COVID-19 patients admitted to Sina Hospital, Tehran University of Medical Sciences from February 20 to May 29, 2020. Patients' data on admission were retrospectively recruited from Sina Hospital COVID-19 Registry (SHCo-19R). Multivariable logistic regression and receiver operating characteristic (ROC) curve analysis were performed to identify the predictive factors for mechanical ventilation. Results: A total of 681 patients were included in the study; 74 patients (10.9%) needed mechanical ventilation during hospitalization, while 607 (89.1%) did not. Multivariate logistic analysis revealed that age (OR,1.049; 95% CI:1.008-1.09), history of diabetes mellitus (OR,3.216; 95% CI:1.134-9.120), respiratory rate (OR,1.051; 95% CI:1.005-1.100), oxygen saturation (OR,0.928; 95% CI:0.872-0.989), CRP (OR,1.013; 95% CI:1.001-1.024) and bicarbonate level (OR,0.886; 95% CI:0.790-0.995) were risk factors for mechanical ventilation during hospitalization. Conclusions: A risk score has been developed based on the available data within the first hours of hospital admission to predict the need for mechanical ventilation. This risk score should be further validated to determine its applicability in other populations.

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
David J. Altschul ◽  
Santiago R. Unda ◽  
Joshua Benton ◽  
Rafael de la Garza Ramos ◽  
Phillip Cezayirli ◽  
...  

Abstract COVID-19 is commonly mild and self-limiting, but in a considerable portion of patients the disease is severe and fatal. Determining which patients are at high risk of severe illness or mortality is essential for appropriate clinical decision making. We propose a novel severity score specifically for COVID-19 to help predict disease severity and mortality. 4711 patients with confirmed SARS-CoV-2 infection were included. We derived a risk model using the first half of the cohort (n = 2355 patients) by logistic regression and bootstrapping methods. The discriminative power of the risk model was assessed by calculating the area under the receiver operating characteristic curves (AUC). The severity score was validated in a second half of 2356 patients. Mortality incidence was 26.4% in the derivation cohort and 22.4% in the validation cohort. A COVID-19 severity score ranging from 0 to 10, consisting of age, oxygen saturation, mean arterial pressure, blood urea nitrogen, C-Reactive protein, and the international normalized ratio was developed. A ROC curve analysis was performed in the derivation cohort achieved an AUC of 0.824 (95% CI 0.814–0.851) and an AUC of 0.798 (95% CI 0.789–0.818) in the validation cohort. Furthermore, based on the risk categorization the probability of mortality was 11.8%, 39% and 78% for patient with low (0–3), moderate (4–6) and high (7–10) COVID-19 severity score. This developed and validated novel COVID-19 severity score will aid physicians in predicting mortality during surge periods.


2021 ◽  
Vol 108 (Supplement_6) ◽  
Author(s):  
N Arora

Abstract Aim To validate the use of RUSHu score in prediction of humerus non union. Method All patients having radiographs of humerus performed between Jan 2016 to December 2018 were assessed based on inclusion and exclusion criteria. The RUSHu scoring system as published was used to score each 6-week radiograph, separately by 2 blinded observers. 6 months was used as end point to assess outcome. Cohort of 188 observations were used to assess utility of scoring system to predict non union. Results 94 suitable fractures were identified. Union rate of 72.3% was observed. Mean score in union group was 9.6, 6.4 for non-unions. There was substantial inter-observer reliability with an ICC of 0.73. Rate of union progressively increases with increasing RUSHu scores. ROC curve analysis identifies 8 as most suitable for use as threshold. Area under the curve is high (0.9) Conclusions A low RUSH score at 6 weeks is a reliable predictor of non union down the line. If a score 7 or lower is observed, it should trigger a discussion with the patient and review of correctable factors contributing to development of non union. Consideration of surgical fixation should be made at this stage if instability is felt to be a major contributing cause. A patient with score of 8 or higher is more likely to go on to union. Routine use of RUSHu score can aid in clinical decision making and introduce an element of objectivity in clinical assessment. It has potential to prompt earlier intervention and reduce morbidity duration.


2020 ◽  
Author(s):  
David Altschul ◽  
Santiago R Unda ◽  
Joshua Benton ◽  
Rafael de La Garza Ramos ◽  
Mark Mehler ◽  
...  

Abstract IntroductionCOVID-19 is commonly mild and self-limiting, but in a considerable portion of patients the disease is severe and fatal. Determining which patients are at high risk of severe illness or mortality is essential for appropriate clinical decision making. We propose a novel severity score specifically for COVID-19 to help predict disease severity and mortality.Methods4,711 patients with confirmed SARS-CoV-2 infection were included. We derived a risk model using the first half of the cohort (n=2,355 patients) by logistic regression and bootstrapping methods. The discriminative power of the risk model was assessed by calculating the area under the receiver operating characteristic curves (AUC). The severity score was validated in a second half of 2,356 patients.ResultsMortality incidence was 26.4% in the derivation cohort and 22.4% in the validation cohort. A COVID-19 severity score ranging from 0 to 10, consisting of age, oxygen saturation, mean arterial pressure, blood urea nitrogen, C-Reactive protein, and the international normalized ratio was developed. A ROC curve analysis was performed in the derivation cohort achieved an AUC of 0.824 (95% CI 0.814-0.851) and an AUC of 0.798 (95% CI 0.789-0.818) in the validation cohort. Furthermore, based on the risk categorization the probability of mortality was 11.8%, 39% and 78% for patient with low (0-3), moderate (4-6) and high (7-10) COVID-19 severity score.ConclusionThis developed and validated novel COVID-19 severity score will aid physicians in predicting mortality during surge periods.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Joonho Park ◽  
Hyeyoon Kim ◽  
So Yeon Kim ◽  
Yeonjae Kim ◽  
Jee-Soo Lee ◽  
...  

AbstractThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected over forty million patients worldwide. Although most coronavirus disease 2019 (COVID-19) patients have a good prognosis, some develop severe illness. Markers that define disease severity or predict clinical outcome need to be urgently developed as the mortality rate in critical cases is approximately 61.5%. In the present study, we performed in-depth proteome profiling of undepleted plasma from eight COVID-19 patients. Quantitative proteomic analysis using the BoxCar method revealed that 91 out of 1222 quantified proteins were differentially expressed depending on the severity of COVID-19. Importantly, we found 76 proteins, previously not reported, which could be novel prognostic biomarker candidates. Our plasma proteome signatures captured the host response to SARS-CoV-2 infection, thereby highlighting the role of neutrophil activation, complement activation, platelet function, and T cell suppression as well as proinflammatory factors upstream and downstream of interleukin-6, interleukin-1B, and tumor necrosis factor. Consequently, this study supports the development of blood biomarkers and potential therapeutic targets to aid clinical decision-making and subsequently improve prognosis of COVID-19.


2013 ◽  
Vol 04 (02) ◽  
pp. 212-224 ◽  
Author(s):  
M. Kashiouris ◽  
J.C. O’Horo ◽  
B.W. Pickering ◽  
V. Herasevich

SummaryContext: Healthcare Electronic Syndromic Surveillance (ESS) is the systematic collection, analysis and interpretation of ongoing clinical data with subsequent dissemination of results, which aid clinical decision-making.Objective: To evaluate, classify and analyze the diagnostic performance, strengths and limitations of existing acute care ESS systems.Data Sources: All available to us studies in Ovid MEDLINE, Ovid EMBASE, CINAHL and Scopus databases, from as early as January 1972 through the first week of September 2012.Study Selection: Prospective and retrospective trials, examining the diagnostic performance of inpatient ESS and providing objective diagnostic data including sensitivity, specificity, positive and negative predictive values.Data Extraction: Two independent reviewers extracted diagnostic performance data on ESS systems, including clinical area, number of decision points, sensitivity and specificity. Positive and negative likelihood ratios were calculated for each healthcare ESS system. A likelihood matrix summarizing the various ESS systems performance was created.Results: The described search strategy yielded 1639 articles. Of these, 1497 were excluded on abstract information. After full text review, abstraction and arbitration with a third reviewer, 33 studies met inclusion criteria, reporting 102,611 ESS decision points. The yielded I2 was high (98.8%), precluding meta-analysis. Performance was variable, with sensitivities ranging from 21% –100% and specificities ranging from 5%-100%.Conclusions: There is significant heterogeneity in the diagnostic performance of the available ESS implements in acute care, stemming from the wide spectrum of different clinical entities and ESS systems. Based on the results, we introduce a conceptual framework using a likelihood ratio matrix for evaluation and meaningful application of future, frontline clinical decision support systems.Citation: Kashiouris M, O’Horo JC, Pickering BW, Herasevich V. Diagnostic performance of electronic syndromic surveillance systems in acute care – a systematic review. Appl Clin Inf 2013; 4: 212–224http://dx.doi.org/10.4338/ACI-2012-12-RA-0053


2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi137-vi137
Author(s):  
Jonathan Zeng ◽  
Kimberly DeVries ◽  
Andra Krauze

Abstract PURPOSE Glioblastomas (GBM) are the most common primary brain tumour recurring in most patients despite maximal management. Patient selection for appropriate treatment modality remains challenging resulting in heterogeneity in management. We examined the patterns of failure and developed a scoring system for patient stratification to optimise clinical decision making. METHODS 822 adults (BC Cancer Agency registry) diagnosed 2005–2015 age ≥60 with histologically confirmed GBM ICD-O-3 codes (9440/3, 9441/3, 9442/3) were reviewed. Univariate and Kaplan-Meier analysis were performed. Performance status (PS), age and resection status were assigned a score, cummulative maximal (favorable) score of 10 and minimum (unfavorable) score of 3. Patterns of failure were further analysed in the subset of patients with radiographic follow-up. RESULTS PS score of 3(KPS >80, ECOG 0/1), 2 (KPS 60–70, ECOG 2), 1 (KPS < 60, ECOG 3/4) (median OS 11, 6, 3 months respectively), age score and resection status were prognostic for OS with PS resulting in the most significant curve separation (p< 0.0001). Biopsy as compared to STR/GTR resulted in poorer OS in patients over 70 (age score 1/2) but had less impact in patients younger than 70 (age scores 3/4). The median OS for cumulative scores of 9/10 (123 patients), 7/8 (286 patients), 5/6 (313 patients), and 3/4 (55 patients) were 14, 8, 4 and 2 months respectively (p< 0.0001) allowing for stratification into 4 prognostic groups. 133 patients had >3 MRIs following diagnosis allowing for clinical and radiographic analysis of progression. Clinical/radiographic progression occurred within 3 months (29%/45%), 6 months (50%/66%), 9 months (70%/81%). Progression type (radiographic, clinical, both was not associated with OS. CONCLUSION Our novel prognostic scoring system is effective in achieving patient stratification and may guide clinical decision making. Early radiographic progression appears to precede clinical deterioration and may represent true progression in the elderly.


Author(s):  
Oguz Akbilgic ◽  
Ramin Homayouni ◽  
Kevin Heinrich ◽  
Max Raymond langham, Jr ◽  
Robert Lowell Davis

Text fields in electronic medical records (EMR) contain information on important factors that influence health outcomes, however, they are underutilized in clinical decision making due to their unstructured nature. We analyzed 6,497 inpatient surgical cases with 719,308 free text notes from Le Bonheur Children&rsquo;s Hospital EMR. We used a text mining approach on preoperative notes to obtain the text-based risk score algorithm as predictive of death within 30 days of surgery. We studied the additional performance obtained by including text-based risk score as a predictor of death along with other structured data based clinical risk factors. The C-statistic of a logistic regression model with 5-fold cross-validation significantly improved from 0.76 to 0.92 when text-based risk scores were included in addition to structured data. We conclude that preoperative free text notes in EMR include significant information that can predict adverse surgery outcomes.


PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248477
Author(s):  
Khushal Arjan ◽  
Lui G. Forni ◽  
Richard M. Venn ◽  
David Hunt ◽  
Luke Eliot Hodgson

Objectives of the study Demographic changes alongside medical advances have resulted in older adults accounting for an increasing proportion of emergency hospital admissions. Current measures of illness severity, limited to physiological parameters, have shortcomings in this cohort, partly due to patient complexity. This study aimed to derive and validate a risk score for acutely unwell older adults which may enhance risk stratification and support clinical decision-making. Methods Data was collected from emergency admissions in patients ≥65 years from two UK general hospitals (April 2017- April 2018). Variables underwent regression analysis for in-hospital mortality and independent predictors were used to create a risk score. Performance was assessed on external validation. Secondary outcomes included seven-day mortality and extended hospital stay. Results Derivation (n = 8,974) and validation (n = 8,391) cohorts were analysed. The model included the National Early Warning Score 2 (NEWS2), clinical frailty scale (CFS), acute kidney injury, age, sex, and Malnutrition Universal Screening Tool. For mortality, area under the curve for the model was 0.79 (95% CI 0.78–0.80), superior to NEWS2 0.65 (0.62–0.67) and CFS 0.76 (0.74–0.77) (P<0.0001). Risk groups predicted prolonged hospital stay: the highest risk group had an odds ratio of 9.7 (5.8–16.1) to stay >30 days. Conclusions Our simple validated model (Older Persons’ Emergency Risk Assessment [OPERA] score) predicts in-hospital mortality and prolonged length of stay and could be easily integrated into electronic hospital systems, enabling automatic digital generation of risk stratification within hours of admission. Future studies may validate the OPERA score in external populations and consider an impact analysis.


2019 ◽  
Vol 130 (5) ◽  
pp. 1528-1537 ◽  
Author(s):  
Georgios A. Zenonos ◽  
Juan C. Fernandez-Miranda ◽  
Debraj Mukherjee ◽  
Yue-Fang Chang ◽  
Klea Panayidou ◽  
...  

OBJECTIVEThere are currently no reliable means to predict the wide variability in behavior of clival chordoma so as to guide clinical decision-making and patient education. Furthermore, there is no method of predicting a tumor’s response to radiation therapy.METHODSA molecular prognostication panel, consisting of fluorescence in situ hybridization (FISH) of the chromosomal loci 1p36 and 9p21, as well as immunohistochemistry for Ki-67, was prospectively evaluated in 105 clival chordoma samples from November 2007 to April 2016. The results were correlated with overall progression-free survival after surgery (PFSS), as well as progression-free survival after radiotherapy (PFSR).RESULTSAlthough Ki-67 and the percentages of tumor cells with 1q25 hyperploidy, 1p36 deletions, and homozygous 9p21 deletions were all found to be predictive of PFSS and PFSR in univariate analyses, only 1p36 deletions and homozygous 9p21 deletions were shown to be independently predictive in a multivariate analysis. Using a prognostication calculator formulated by a separate multivariate Cox model, two 1p36 deletion strata (0%–15% and > 15% deleted tumor cells) and three 9p21 homozygous deletion strata (0%–3%, 4%–24%, and ≥ 25% deleted tumor cells) accounted for a range of cumulative hazard ratios of 1 to 56.1 for PFSS and 1 to 75.6 for PFSR.CONCLUSIONSHomozygous 9p21 deletions and 1p36 deletions are independent prognostic factors in clival chordoma and can account for a wide spectrum of overall PFSS and PFSR. This panel can be used to guide management after resection of clival chordomas.


Informatics ◽  
2019 ◽  
Vol 6 (1) ◽  
pp. 4 ◽  
Author(s):  
Oguz Akbilgic ◽  
Ramin Homayouni ◽  
Kevin Heinrich ◽  
Max Langham ◽  
Robert Davis

Text fields in electronic medical records (EMR) contain information on important factors that influence health outcomes, however, they are underutilized in clinical decision making due to their unstructured nature. We analyzed 6497 inpatient surgical cases with 719,308 free text notes from Le Bonheur Children’s Hospital EMR. We used a text mining approach on preoperative notes to obtain a text-based risk score to predict death within 30 days of surgery. In addition, we evaluated the performance of a hybrid model that included the text-based risk score along with structured data pertaining to clinical risk factors. The C-statistic of a logistic regression model with five-fold cross-validation significantly improved from 0.76 to 0.92 when text-based risk scores were included in addition to structured data. We conclude that preoperative free text notes in EMR include significant information that can predict adverse surgery outcomes.


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