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

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.

Download Full-text

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

BMC Infectious Diseases ◽

10.1186/s12879-020-05614-2 ◽

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.

Download Full-text

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

10.21203/rs.3.rs-34708/v4 ◽

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

Download Full-text

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

10.21203/rs.3.rs-34708/v3 ◽

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.

Download Full-text

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

10.21203/rs.3.rs-34708/v2 ◽

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.

Download Full-text

Abstract P165: Comparing Grip Strength, Weakness, And Frailty Based On Comorbidity And Laboratory Data Among Patients With Cardiovascular Disease

Circulation ◽

10.1161/circ.141.suppl_1.p165 ◽

2020 ◽

Vol 141 (Suppl_1) ◽

Author(s):

Gloria Kim ◽

Arati A Gangadharan ◽

Matthew A Corriere

Keyword(s):

Grip Strength ◽

Frailty Index ◽

Laboratory Data ◽

Categorical Variables ◽

P Value ◽

Continuous Variables ◽

Chi Square ◽

Mean Values ◽

Multivariable Models ◽

And Gender

Introduction: Some approaches to frailty screening use diagnostic or laboratory data that may be incomplete. Grip strength can identify weakness, a component of phenotype-based frailty assessment. We compared grip strength as a reductionist, phenotype-based approach to frailty screening with comorbidity and laboratory-based alternatives. Hypothesis: Grip strength and categorical weakness are correlated with the modified frailty index-5 (mFI-5) and lab values associated with frailty. Methods: Weakness based on grip, BMI, and gender was compared with mFI-5 comorbidities and lab values. Patients with at least 3/5 mFI-5 comorbidities were considered frail. Lab data collected within 6 months of grip measurement was assessed. Associations were evaluated using multivariable models and kappa. Methods: 2,597 patients had grip strength measured over 5 months. Mean age was 64.4±14.6, mean BMI was 29.5±6.9;46% were women, and 87% white. Prevalent comorbidities included hypertension (28%), CHF (22%), diabetes (29%), and COPD (26%); 9% were functionally dependent. 34% were weak, but only 13% were frail based on mFI-5. Hemoglobin, creatinine, and CRP differed significantly based on weakness ( Table ). Laboratory data were missing for 36%- 95% of patients. Multivariable models identified significant associations between weakness, hemoglobin, and all MFI-5 comorbidities. Categorical agreement between weakness and frailty was limited (kappa =0.09; 95% CL 0.0641-0.1232). Conclusion: Weakness based on grip strength provides a practical, inexpensive approach to risk assessment, especially when incomplete data excludes other approaches. Comorbidity-based assessment categorizes many weak patients as non-frail. Table. Demographic, laboratory values, and comorbidities by categorical weakness based on grip 20 th percentile. Mean values for continuous variables by weakness adjusted for gender and BMI, p-value for T-test; frequency and total percent for categorical variables, p-value represents chi-square test.

Download Full-text

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

10.32592/ircmj.2021.23.2.283 ◽

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.

Download Full-text

Prediction of anemia at delivery

Scientific Reports ◽

10.1038/s41598-021-85622-7 ◽

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.

Download Full-text

617 Who Benefits the Most from Burn Camps?

Journal of Burn Care & Research ◽

10.1093/jbcr/irab032.267 ◽

2021 ◽

Vol 42 (Supplement_1) ◽

pp. S162-S163

Author(s):

Jennifer B Radics-Johnson ◽

Daniel W Chacon ◽

Li Zhang

Keyword(s):

Retrospective Review ◽

Burn Injury ◽

Statistical Significance ◽

Categorical Variables ◽

P Value ◽

Continuous Variables ◽

Self Evaluation ◽

Significant Difference ◽

Return Home ◽

The Impact

Abstract Introduction Burn camps provide a unique environment and activities for children that have experienced a burn-injury. Positive outcomes from attending burn camp include increased self-esteem, decreased feelings of isolation and a greater sense of self-confidence. In a 3-year retrospective review of camper evaluations from one of the largest and longest running week-long burn camps in the nation for ages 5–17, we aimed to assess if a child’s gender, age, TBSA or ethnicity affected the impact that burn camp had on a child. Methods A 3-year retrospective review of a Burn Camp’s camper evaluation forms was conducted for campers that attended burn camp between 2017–2019. Camp rosters were reviewed to determine the camper gender, age, TBSA and ethnicity. Camper self-evaluation forms completed at the end of each camp session were reviewed to record camper responses to questions regarding their opinions on the impact camp had on them as well as how camp will impact their lives once they return home. Categorical variables were summarized as frequency and percentage, and continuous variables were described as median and range. To check the relationship between two categorical variables, Chi-square test was used. To compare the continuous variable among groups, Kruskal-Wallis ANOVA was used. Statistical significance was declared based on a p value< 0.5. Results Within 2017–2019, there were 413 camper records. Participants’ demographic characteristics are summarized in Table 1. There were 208 males (50.3%) and 205 females (49.6%). The median age of campers were 11.86, 12.44 and 12.45 for 2017–2019, with the range from 5.16 years to 17.96 years. The median TBSA were 20, 20 and 18 for 2017–2019, with the range from 0.08 to 90. Collectively there were 47.7% Hispanic (n= 197); 24.2% Whites (n=100); 13.1% Black (n= 54); 4.6% Asian (n=19) and 7.7% Other (n=32). There were 395 camper self-evaluation forms submitted. Results of three questions there we were interested in are summarized collectively in Table 2. 57% of campers responded, “Yes, Definitely” to the question “After going to this event, will you feel more comfortable being around your classmates or friends?” 54% responded, “ Yes, Definitely” to the question “Do you feel more confidents in sharing your burn story with others when returning home?” and 51% responded “Yes, Definitely” to “Did you learn anything that will help you when you return home?” Conclusions In analyzing the camper responses, there was no statistically significant difference in responses comparing gender, age, TBSA or ethnicity.

Download Full-text

POS0942 THE NEGATIVE IMPACT OF UNDIAGNOSED DEPRESSION IN AXIAL SPONDYLOARTHROPARTHY: RESULTS OF A SCREENING STUDY

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.145 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 734.1-734

Author(s):

S. Maguire ◽

F. B. O’shea

Keyword(s):

Disease Activity ◽

Negative Impact ◽

Depression Scale ◽

Clinic Visit ◽

Categorical Variables ◽

P Value ◽

Continuous Variables ◽

Numerical Result ◽

Co Morbidity ◽

Patient Reported

Background:Previous research in axial spondyloarthropathy(axSpA) has shown this population to have a high prevalence of depression. This co-morbidity has been previously shown to impact disease activity in patients with rheumatic disease.Objectives:The purpose of this study was to screen for early signs of depression using two validated tools, the Patient Health Questionaire-9 (PHQ-9) and the Hospital Anxiety and Depression Scale for depression (HADs-D) in patients with known axSpA.Methods:AxSpA patients attending the Rheumatology department in St James’ Hospital between February and October 2020 were invited to take a self-administered survey which included the PHQ-9 and the HADs-D. Scores from the HADs-D yielded a numerical result which was then categorised as normal, borderline or abnormal. PHQ-9 numerical results were categorised as normal, mild, moderate, moderate/severe or severe. Patients with a known diagnosis of depression were excluded. In addition to baseline demographics, patient reported outcomes from the clinic visit were also recorded.Data analysis was performed using IBM SPSS version 26. Continuous variables were recorded as means, categorical variables as frequencies with percentages. A one-way analysis of variance analysis (ANOVA) was used to determine significance of variation in outcomes between patient outcomes as determined by the HADs-D and PHQ-9. A p-value of <0.05 was deemed significant. Consent was obtained prior to participation. Approval was received from the St James’/Tallaght Hospital Joint Ethics Committee.Results:In total 71 axSpA patients took part in the survey. The population was 70.4%(50) males and 29.5%(21) female, with an average age 47.9 years and mean disease duration 19.7 years (mean outcomes: BASDAI 4.08, BASFI 3.62, BASMI 3.54, ASQoL 6.79). Overall, 7 (9.9%) participants recorded abnormal HADs-D scores, while 17 (23.9%) recorded moderate to severe PHQ-9 scores indicative of underlying depression. AxSpA females had higher mean HADs-D scores (7.5 vs 4.8, p=0.01) than males, with abnormal scores in 19%(4) of females and 6% (3) of males. No significant differences were found in PHQ-9 scores between genders.Analysis revealed significantly worse BASDAI (6.27 vs 3.42, p<0.01) and AQoL scores (12.57 vs 5.26, p<0.01) in axSpA patients with abnormal compared to normal HADs-D scores. No significant differences were noted in BASFI, BASMI or baseline demographics. A similar pattern was noted on analysis of PHQ-9 scores, with significantly worse BASDAI (7.9 vs 2.55, p<0.01), BASFI (8.05 vs 2.33, p<0.01) and ASQoL (19.5 vs 2.62, p<0.01) noted in those scoring as severe compared to normal. No significant differences were detected in BASMI scores or baseline demographics.Conclusion:A high percentage of axSpA patients recorded high HADs-D and PHQ-9 scores concerning for undiagnosed depression. These patients were noted to have significantly worse disease activity and quality of life as compared to patients with normal scores. Clinicians treating axSpA should consider screening for depression in this population.Disclosure of Interests:Sinead Maguire Speakers bureau: Speaker fee from Jassen, Grant/research support from: Recipient of the Gilead Inflammation Fellowship Grant, Finbar Barry O’Shea: None declared

Download Full-text

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

Journal of Perinatal Medicine ◽

10.1515/jpm-2015-0287 ◽

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.

Download Full-text