scholarly journals A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19 patients

2020 ◽  
Author(s):  
Xiaojun Ma ◽  
Huifang Wang ◽  
Junwei Huang ◽  
Yan Geng ◽  
Shuqi Jiang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Characteristic Curve ◽  
Laboratory Data ◽  
Categorical Variables ◽  
Continuous Variables ◽  
Exact Test ◽  
Model Based ◽  
Chronic Heart Disease ◽  
Independent Risk Factors ◽  
D Dimer

Abstract Background and AimCOVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission.Methods COVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson’s χ2-test or Fisher’s exact test, and continuous variables were analyzed using Student’s t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the log-binomial model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method.Results A total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (relative risk [RR]: 0.905, 95% confidence interval [CI]: 0.868-0.944; P<0.001), chronic heart disease (CHD, RR: 0.045, 95% CI: 0.0097-0.205; P<0.001, the percentage of lymphocytes (Lym%, RR: 1.125, 95% CI: 1.041-1.216; P=0.0029), platelets (RR: 1.008, 95% CI: 1.003-1.012; P=0.001), C-reaction protein (RR: 0.982, 95% CI: 0.973-0.991; P<0.001), lactate dehydrogenase (LDH, RR: 0.993, 95% CI: 0.990-0.997; P<0.001) and D-dimer (RR: 0.734, 95% CI: 0.617-0.879; P<0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923-0.973). ConclusionA nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.

scholarly journals A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19 patients

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Xiaojun Ma ◽  
Huifang Wang ◽  
Junwei Huang ◽  
Yan Geng ◽  
Shuqi Jiang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Bootstrap Method ◽  
Characteristic Curve ◽  
Laboratory Data ◽  
Categorical Variables ◽  
Continuous Variables ◽  
Exact Test ◽  
Model Based ◽  
Independent Risk Factors ◽  
D Dimer

Abstract Background COVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission. Methods COVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson’s χ2-test or Fisher’s exact test, and continuous variables were analyzed using Student’s t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the log-binomial model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method. Results A total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (relative risk [RR]: 0.905, 95% confidence interval [CI]: 0.868–0.944; P < 0.001), chronic heart disease (CHD, RR: 0.045, 95% CI: 0.0097–0.205; P < 0.001, the percentage of lymphocytes (Lym%, RR: 1.125, 95% CI: 1.041–1.216; P = 0.0029), platelets (RR: 1.008, 95% CI: 1.003–1.012; P = 0.001), C-reaction protein (RR: 0.982, 95% CI: 0.973–0.991; P < 0.001), lactate dehydrogenase (LDH, RR: 0.993, 95% CI: 0.990–0.997; P < 0.001) and D-dimer (RR: 0.734, 95% CI: 0.617–0.879; P < 0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923–0.973). Conclusions A nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.

scholarly journals A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19 patients

2020 ◽  
Author(s):  
Xiaojun Ma ◽  
Huifang Wang ◽  
Junwei Huang ◽  
Yan Geng ◽  
Shuqi Jiang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Bootstrap Method ◽  
Characteristic Curve ◽  
Laboratory Data ◽  
Categorical Variables ◽  
Continuous Variables ◽  
Exact Test ◽  
Model Based ◽  
Independent Risk Factors ◽  
D Dimer

Abstract BackgroundCOVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission.MethodsCOVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson’s χ2-test or Fisher’s exact test, and continuous variables were analyzed using Student’s t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the log-binomial model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method.ResultsA total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (relative risk [RR]: 0.905, 95% confidence interval [CI]: 0.868-0.944; P<0.001), chronic heart disease (CHD, RR: 0.045, 95% CI: 0.0097-0.205; P<0.001, the percentage of lymphocytes (Lym%, RR: 1.125, 95% CI: 1.041-1.216; P=0.0029), platelets (RR: 1.008, 95% CI: 1.003-1.012; P=0.001), C-reaction protein (RR: 0.982, 95% CI: 0.973-0.991; P<0.001), lactate dehydrogenase (LDH, RR: 0.993, 95% CI: 0.990-0.997; P<0.001) and D-dimer (RR: 0.734, 95% CI: 0.617-0.879; P<0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923-0.973).ConclusionsA nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary

scholarly journals A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19 patients

2020 ◽  
Author(s):  
Xiaojun Ma ◽  
Huifang Wang ◽  
Junwei Huang ◽  
Yan Geng ◽  
Shuqi Jiang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Bootstrap Method ◽  
Characteristic Curve ◽  
Laboratory Data ◽  
Categorical Variables ◽  
Continuous Variables ◽  
Exact Test ◽  
Model Based ◽  
Independent Risk Factors ◽  
D Dimer

Abstract Background and AimCOVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission.MethodsCOVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson’s χ2-test or Fisher’s exact test, and continuous variables were analyzed using Student’s t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the log-binomial model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method.ResultsA total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (relative risk [RR]: 0.905, 95% confidence interval [CI]: 0.868-0.944; P<0.001), chronic heart disease (CHD, RR: 0.045, 95% CI: 0.0097-0.205; P<0.001, the percentage of lymphocytes (Lym%, RR: 1.125, 95% CI: 1.041-1.216; P=0.0029), platelets (RR: 1.008, 95% CI: 1.003-1.012; P=0.001), C-reaction protein (RR: 0.982, 95% CI: 0.973-0.991; P<0.001), lactate dehydrogenase (LDH, RR: 0.993, 95% CI: 0.990-0.997; P<0.001) and D-dimer (RR: 0.734, 95% CI: 0.617-0.879; P<0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923-0.973).ConclusionA nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.

scholarly journals A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19 patients

2020 ◽  
Author(s):  
Xiaojun Ma ◽  
Huifang Wang ◽  
Junwei Huang ◽  
Yan Geng ◽  
Shuqi Jiang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Bootstrap Method ◽  
Characteristic Curve ◽  
Laboratory Data ◽  
Categorical Variables ◽  
P Value ◽  
Continuous Variables ◽  
Model Based ◽  
Independent Risk Factors ◽  
D Dimer

Abstract Background and Aim COVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission. Methods COVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson’s χ2-test or Fisher’s exact test, and continuous variables were analyzed using Student’s t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the multivariate model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method. Results A total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (odds ratio [OR]: 0.905, 95% confidence interval [CI]: 0.868-0.944; P<0.001), chronic heart disease (CHD, OR: 0.048, 95% CI: 0.013-0.180; P<0.001), the percentage of lymphocytes (Lym%, OR: 1.116, 95% CI: 1.051-1.184; P<0.001), platelets (OR: 1.008, 95% CI: 1.003-1.012; P=0.001), C-reaction protein (OR: 0.982, 95% CI: 0.973-0.991; P<0.001), lactate dehydrogenase (LDH, OR: 0.993, 95% CI: 0.990-0.997; P<0.001) and D-dimer (OR: 0.734, 95% CI: 0.615-0.875; P=0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923-0.973). Conclusion A nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.

The Predictive Value of Thromboelastogram in the Evaluation of Patients with Suspected Acute Venous Thromboembolism

2019 ◽  
Vol 143 (3) ◽  
pp. 272-278
Author(s):  
Tareq Abu Assab ◽  
David Raveh-Brawer ◽  
Julia Abramowitz ◽  
Mira Naamad ◽  
Chezi Ganzel
Keyword(s):  
Venous Thromboembolism ◽  
Emergency Room ◽  
Alpha Angle ◽  
Categorical Variables ◽  
Clot Lysis ◽  
Fisher Exact Test ◽  
Continuous Variables ◽  
Exact Test ◽  
Significant Difference ◽  
D Dimer

Introduction: The objective of this prospective study was to examine whether thromboelastogram (TEG) can predict the presence of venous thromboembolism (VTE) in patients who arrive at the emergency room with signs/symptoms that raise the suspicion of acute VTE. Methods: Every patient was tested for D-dimer and all TEG parameters, including: reaction time, clot time formation, alpha-angle, maximal amplitude, clot viscoelasticity, coagulation index, and clot lysis at 30 min. For categorical variables, χ2 or the Fisher exact test were used, and for continuous variables the t test or other non-parametric tests were used. Results: During 2016, a total of 109 patients were enrolled with a median age of 55.7 (21–89) years. Eighteen patients were diagnosed with VTE. Analyzing the different TEG parameters, both as continuous and categorical variables, did not reveal a statistically significant difference between VTE-positive and VTE-negative patients. Combining different TEG parameters or dividing the cohort according to gender, clinical suspicion of VTE (Well’s criteria), or different levels of D-dimer did not change the results of the analysis. Conclusion: The current study could not demonstrate a significant value of any TEG parameter as a predictor of VTE among patients who came to the emergency room with signs/symptoms that raise the suspicion of VTE.

scholarly journals Clinical and Laboratory Factors Associated with Severe Disease Course in Turkish Patients with SARS-COV-2 Infection

2021 ◽  
Keyword(s):  
Risk Factors ◽  
Severe Disease ◽  
Laboratory Data ◽  
Course Of Disease ◽  
Neutrophil Lymphocyte Ratio ◽  
Reactive Protein ◽  
Pulmonary Infiltration ◽  
Independent Risk Factors ◽  
Turkish Patients ◽  
D Dimer

Background: Coronavirus disease 2019 (COVID-19) can demonstrate different clinical spectra. Objectives: The current study aimed to analyze the clinical and laboratory risk factors of the severe course of disease in patients with COVID-19. Materials and Methods: Consecutive patients with a diagnosis of COVID-19 pneumonia were included in the present study. The demographic characteristics, comorbid diseases, symptoms, chest computed tomography (CT) findings, laboratory data, oxygen saturation (SpO2), and body temperature of the patients were recorded. The coexistence of pulmonary infiltration in CT and SpO2 of ≤ %93 on fingertip pulse oximeter was defined as the severe course of the disease. Results: A total of 475 patients were included in the current study. The mean age of the patients was 52.02±15.9 years, and 259 (54.5%) participants were male. The disease was mild and severe in 80% (n=380) and 20% (n=95) of the patients. The age of > 50 years, coexistence of hypertension (HT) and diabetes mellitus (DM), neutrophil/lymphocyte ratio (NLR) of > 4, high lactate dehydrogenase (LDH) of > 240 U/L, C-reactive protein (CRP) of > 8 mg/dL, and D-dimer of ≥ 1000 ng/mL were determined to be the risk factors for the severe course of the disease. Conclusion: Age, NLR, CRP, LDH, D-dimer, comorbidity, and coexistence of DM and HT were the independent risk factors for the severe course of the disease. The aforementioned factors should be taken into account during risk stratification and management of patients with COVID-19.

scholarly journals Prediction of anemia at delivery

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Enav Yefet ◽  
Avishag Yossef ◽  
Zohar Nachum
Keyword(s):  
Risk Factors ◽  
Operating Characteristic ◽  
Complete Blood Count ◽  
Characteristic Curve ◽  
Secondary Analysis ◽  
Area Under The Curve ◽  
Iron Supplement ◽  
Independent Risk Factors ◽  
Operating Characteristic Curve ◽  
Study Database

AbstractWe aimed to assess risk factors for anemia at delivery by conducting a secondary analysis of a prospective cohort study database including 1527 women who delivered vaginally ≥ 36 gestational weeks. Anemia (Hemoglobin (Hb) < 10.5 g/dL) was assessed at delivery. A complete blood count results during pregnancy as well as maternal and obstetrical characteristics were collected. The primary endpoint was to determine the Hb cutoff between 24 and 30 gestational weeks that is predictive of anemia at delivery by using the area under the curve (AUC) of the receiver operating characteristic curve. Independent risk factors for anemia at delivery were assessed using stepwise multivariable logistic regression. Hb and infrequent iron supplement treatment were independent risk factors for anemia at delivery (OR 0.3 95%CI [0.2–0.4] and OR 2.4 95%CI [1.2–4.8], respectively; C statistics 83%). Hb 10.6 g/dL was an accurate cutoff to predict anemia at delivery (AUC 80% 95%CI 75–84%; sensitivity 75% and specificity 74%). Iron supplement was beneficial to prevent anemia regardless of Hb value. Altogether, Hb should be routinely tested between 24 and 30 gestational weeks to screen for anemia. A flow chart for anemia screening and treatment during pregnancy is proposed in the manuscript.Trial registration: ClinicalTrials.gov Identifier: NCT02434653.

scholarly journals Higher D-dimer level in the early third trimester predicts the occurrence of postpartum hemorrhage

2016 ◽  
Vol 44 (5) ◽  
Author(s):  
Naho Endo-Kawamura ◽  
Mana Obata-Yasuoka ◽  
Hiroya Yagi ◽  
Rena Ohara ◽  
Yuko Nagai ◽  
...  
Keyword(s):  
Risk Factors ◽  
Postpartum Hemorrhage ◽  
Blood Parameters ◽  
Demographic Characteristics ◽  
Activity Level ◽  
Late Gestation ◽  
Antithrombin Activity ◽  
Estimated Blood Loss ◽  
Independent Risk Factors ◽  
D Dimer

AbstractThis study aimed to determine effective predictive factors for primary postpartum hemorrhage (PPH) among clinical blood parameters associated with coagulation and fibrinolysis and demographic characteristics.We retrospectively studied 1032 women who underwent determinations of clinical blood parameters at gestational week (GW) 29–32 and GW 35–37 and gave birth to singleton infants at our hospital between January 2011 and December 2013. PPH was defined as estimated blood loss ≥700 mL. Multivariate logistic regression analyses were used to determine independent risk factors and odds ratios (OR) for PPH.PPH occurred in 104 of 1032 women (10%). Three blood variables, fibrinogen level <4.0 g/L (OR [95% CI], 1.96 [1.18–3.27]), antithrombin activity <85% of normal activity level (1.84 [1.05–3.21]), and D-dimer level >2.7 μg/mL (2.03 [1.29–3.19]) at GW 35–37, and three demographic characteristics, maternal age ≥35 years (1.75 [1.15–2.68]), BMI >28.2 kg/mAmong blood parameters, higher D-dimer levels and lower levels of antithrombin activity and fibrinogen in late gestation were independent risk factors for PPH.

scholarly journals HPV genotyping and risk factors for anal high-risk HPV infection in men who have sex with men from Toronto, Canada

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yoojin Choi ◽  
Mona Loutfy ◽  
Robert S. Remis ◽  
Juan Liu ◽  
Anuradha Rebbapragada ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Hpv Vaccine ◽  
Hpv Infection ◽  
Categorical Variables ◽  
Genotype Distribution ◽  
Continuous Variables ◽  
Chi Square ◽  
Hpv Genotypes ◽  
Sex With Men

AbstractMen who have sex with men (MSM) are disproportionately affected by anal cancer, predominantly caused by high-risk (HR) human papillomavirus (HPV) infection. Currently, the nonavalent HPV vaccine provides coverage against nine HPV genotypes, including seven HR-HPV genotypes. Here, we characterize anal HR-HPV genotype distribution and associated risk factors in MSM from Toronto, Canada recruited between September 2010 and June 2012. Wilcoxon–Mann–Whitney test was used for continuous variables, Chi-square test was performed for categorical variables, and a multivariable model using logistic regression was created to assess for correlates of anal HR-HPV infection. A total of 442 MSM were recruited, with a median age of 45 (IQR 38–50) and an overall HPV prevalence of 82%. The prevalence of any HR-HPV infection was 65.3% and 50.7% in the HIV-positive and HIV-negative MSM, respectively. No participant tested positive for all genotypes covered by the nonavalent vaccine. HIV status (aOR 1.806; 95% CI 1.159–2.816), smoking (aOR 2.176; 95% CI 1.285–3.685) and the number of lifetime sexual partners (aOR 2.466; 95% CI 1.092–5.567) were independent risk factors for anal HR-HPV infection. Our findings will be useful to inform HPV vaccine rollout and HPV prevention strategies in Canadian MSM.

Outcomes For Hand Burns Treated With Autologous Skin Cell Suspension In 20% Tbsa And Smaller Injuries

2021 ◽  
Author(s):  
G Malcolm Taylor ◽  
Scott A Barnett ◽  
Charles T Tuggle ◽  
Jeff E Carter ◽  
Herb A Phelan
Keyword(s):  
Wound Closure ◽  
Exclusion Criterion ◽  
Categorical Variables ◽  
Dermal Substitute ◽  
Continuous Variables ◽  
Exact Test ◽  
Skin Cell ◽  
Returning To Work ◽  
Hand Burns ◽  
Sheet Graft

Abstract Hypothesis In order to address the confounder of TBSA on burn outcomes, we sought to analyze our experience with the use of autologous skin cell suspensions (ASCS) in a cohort of subjects with hand burns whose TBSA totaled 20% or less. We hypothesized that the use of ASCS in conjunction with 2:1 meshed autograft for the treatment of hand burn injuries would provide comparable outcomes to hand burns treated with sheet or minimally meshed autograft alone. Methods A retrospective review was conducted for all deep partial and full thickness hand burns treated with split thickness autograft (STAG) at our urban verified burn center between April, 2018 to September, 2020. Exclusion criterion was a TBSA greater than 20%. The cohorts were those subjects treated with ASCS in combination with STAG (ASCS(+)) versus those treated with STAG alone (ASCS(-)). All ASCS(+) subjects were treated with 2:1 meshed STAG and ASCS overspray while all ASCS(-) subjects had 1:1, piecrust, or unmeshed sheet graft alone. Outcomes measured included demographics, time to wound closure, proportion returning to work (RTW), and length of time to RTW. Mann-Whitney U test was used for comparisons of continuous variables, and Fishers Exact test for categorical variables. Values are reported as medians and 25 th and 75 th interquartile ranges. Results Fifty-one subjects fit the study criteria (ASCS(+) n=31, ASCS(-) n=20). The ASCS(+) group was significantly older than the ASCS(-) cohort (44 yrs [32, 54] vs 32 [27.5, 37], p=0.009) with larger %TBSA burns (15% [9.5, 17] vs 2% [1, 4], p &lt;0.0001), and larger size hand burns (190 cm2 [120, 349.5] vs 126 cm2 [73.5, 182], p=0.015). Comparable results were seen between ASCS(+) and ASCS(-), respectively, for time to wound closure (9 days [7, 13] vs 11.5 [6.75, 14], p=0.63), proportion RTW (61% vs 70%, p=0.56), and days for RTW among those returning (35 [28.5, 57] vs 33 [20.25, 59], p=0.52). The ASCS(+) group had two graft infections with no reoperations, while ASCS(-) had one infection with one reoperation. No subjects in either group had a dermal substitute placed. Conclusion Despite being significantly older, having larger hand wounds, and larger overall wounds within the parameters of the study criteria, patients with 20% TBSA burns or smaller whose hand burns were treated with 2:1 mesh and ASCS overspray had comparable time to wound closure, proportion of returning to work, and time to return to work as subjects treated with 1:1 or pie-crust meshed STAG. Our group plans to follow this work with scar assessments for a more granular picture of pliability and reconstructive needs.

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