scholarly journals Evaluation of Repeated Web-Based Screening for Predicting Postpartum Depression: Prospective Cohort Study

10.2196/26665 ◽  
2021 ◽  
Vol 8 (12) ◽  
pp. e26665
Author(s):  
Kathrin Haßdenteufel ◽  
Katrin Lingenfelder ◽  
Cornelia E Schwarze ◽  
Manuel Feisst ◽  
Katharina Brusniak ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Depressive Symptoms ◽  
High Risk ◽  
Postpartum Depression ◽  
Predictive Accuracy ◽  
Regression Analyses ◽  
Multivariate Logistic Regression ◽  
Web Based ◽  
Screening Algorithm ◽  
Optimal Screening

Background Postpartum depression (PPD) is a severe mental disorder that often results in poor maternal-infant attachment and negatively impacts infant development. Universal screening has recently been recommended to identify women at risk, but the optimal screening time during pregnancy has not been defined so far. Thus, web-based technologies with widespread use among women of childbearing age create new opportunities to detect pregnancies with a high risk for adverse mental health outcomes at an early stage. Objective The aim of this study was to stratify the risk for PPD and to determine the optimal screening time during pregnancy by using a web-based screening tool collecting electronic patient-reported outcomes (ePROs) as the basis for a screening algorithm. Methods In total, 214 women were repeatedly tested for depressive symptoms 5 times during and 3 times after pregnancy by using the Edinburgh Postnatal Depression Scale (EPDS), accessible on a web-based pregnancy platform, developed by the authors of this study. For each prenatal assessment, the area under the curve (AUC), sensitivity, specificity, and predictive values for PPD were calculated. Multivariate logistic regression analyses were applied to identify further potential predictors, such as age, education, parity, relationship quality, and anxiety, to increase predictive accuracy. Results Digitally collected data from 214 pregnant women were analyzed. The predictive accuracy of depressive symptoms 3 and 6 months postpartum was reasonable to good regarding the screening in the second (AUC=0.85) and third (AUC=0.75) trimester. The multivariate logistic regression analyses resulted in an excellent AUC of 0.93 at 3 months and a good AUC of 0.87 at 6 months postpartum. Conclusions The best predictive accuracy for PPD has been shown for screening between the 24th and the 28th gestational week (GW) and seems to be beneficial for identifying women at risk. In combination with the aforementioned predictive factors, the discriminatory power improved, particularly at 3 months postpartum. Screening for depression during pregnancy, combined with the women’s personal risk profile, can be used as a starting point for developing a digital screening algorithm. Thereby, web-based assessment tools constitute feasible, efficient, and cost-effective approaches. Thus, they seem to be beneficial in detecting high-risk pregnancies in order to improve maternal and infant birth outcomes in the long term.

scholarly journals Evaluation of Repeated Web-Based Screening for Predicting Postpartum Depression: Prospective Cohort Study (Preprint)

2020 ◽  
Author(s):  
Kathrin Haßdenteufel ◽  
Katrin Lingenfelder ◽  
Cornelia E Schwarze ◽  
Manuel Feisst ◽  
Katharina Brusniak ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Depressive Symptoms ◽  
High Risk ◽  
Postpartum Depression ◽  
Predictive Accuracy ◽  
Regression Analyses ◽  
Multivariate Logistic Regression ◽  
Web Based ◽  
Screening Algorithm ◽  
Optimal Screening

BACKGROUND Postpartum depression (PPD) is a severe mental disorder that often results in poor maternal-infant attachment and negatively impacts infant development. Universal screening has recently been recommended to identify women at risk, but the optimal screening time during pregnancy has not been defined so far. Thus, web-based technologies with widespread use among women of childbearing age create new opportunities to detect pregnancies with a high risk for adverse mental health outcomes at an early stage. OBJECTIVE The aim of this study was to stratify the risk for PPD and to determine the optimal screening time during pregnancy by using a web-based screening tool collecting electronic patient-reported outcomes (ePROs) as the basis for a screening algorithm. METHODS In total, 214 women were repeatedly tested for depressive symptoms 5 times during and 3 times after pregnancy by using the Edinburgh Postnatal Depression Scale (EPDS), accessible on a web-based pregnancy platform, developed by the authors of this study. For each prenatal assessment, the area under the curve (AUC), sensitivity, specificity, and predictive values for PPD were calculated. Multivariate logistic regression analyses were applied to identify further potential predictors, such as age, education, parity, relationship quality, and anxiety, to increase predictive accuracy. RESULTS Digitally collected data from 214 pregnant women were analyzed. The predictive accuracy of depressive symptoms 3 and 6 months postpartum was reasonable to good regarding the screening in the second (AUC=0.85) and third (AUC=0.75) trimester. The multivariate logistic regression analyses resulted in an excellent AUC of 0.93 at 3 months and a good AUC of 0.87 at 6 months postpartum. CONCLUSIONS The best predictive accuracy for PPD has been shown for screening between the 24th and the 28th gestational week (GW) and seems to be beneficial for identifying women at risk. In combination with the aforementioned predictive factors, the discriminatory power improved, particularly at 3 months postpartum. Screening for depression during pregnancy, combined with the women’s personal risk profile, can be used as a starting point for developing a digital screening algorithm. Thereby, web-based assessment tools constitute feasible, efficient, and cost-effective approaches. Thus, they seem to be beneficial in detecting high-risk pregnancies in order to improve maternal and infant birth outcomes in the long term.

scholarly journals Development and Validation of a Dynamic Nomogram to Predict the Risk of Neonatal White Matter Damage

2021 ◽  
Vol 14 ◽  
Author(s):  
Wenjun Cao ◽  
Chenghan Luo ◽  
Mengyuan Lei ◽  
Min Shen ◽  
Wenqian Ding ◽  
...  
Keyword(s):  
Logistic Regression ◽  
White Matter ◽  
Confidence Interval ◽  
Prediction Model ◽  
Risk Prediction ◽  
Predictive Accuracy ◽  
Risk Prediction Model ◽  
Multivariate Logistic Regression ◽  
White Matter Damage ◽  
Web Based

PurposeWhite matter damage (WMD) was defined as the appearance of rough and uneven echo enhancement in the white matter around the ventricle. The aim of this study was to develop and validate a risk prediction model for neonatal WMD.Materials and MethodsWe collected data for 1,733 infants hospitalized at the Department of Neonatology at The First Affiliated Hospital of Zhengzhou University from 2017 to 2020. Infants were randomly assigned to training (n = 1,216) or validation (n = 517) cohorts at a ratio of 7:3. Multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used to establish a risk prediction model and web-based risk calculator based on the training cohort data. The predictive accuracy of the model was verified in the validation cohort.ResultsWe identified four variables as independent risk factors for brain WMD in neonates by multivariate logistic regression and LASSO analysis, including gestational age, fetal distress, prelabor rupture of membranes, and use of corticosteroids. These were used to establish a risk prediction nomogram and web-based calculator (https://caowenjun.shinyapps.io/dynnomapp/). The C-index of the training and validation sets was 0.898 (95% confidence interval: 0.8745–0.9215) and 0.887 (95% confidence interval: 0.8478–0.9262), respectively. Decision tree analysis showed that the model was highly effective in the threshold range of 1–61%. The sensitivity and specificity of the model were 82.5 and 81.7%, respectively, and the cutoff value was 0.099.ConclusionThis is the first study describing the use of a nomogram and web-based calculator to predict the risk of WMD in neonates. The web-based calculator increases the applicability of the predictive model and is a convenient tool for doctors at primary hospitals and outpatient clinics, family doctors, and even parents to identify high-risk births early on and implementing appropriate interventions while avoiding excessive treatment of low-risk patients.

Predictors of thermal increase in magnetic resonance–guided focused ultrasound treatment for essential tremor: histogram analysis of skull density ratio values for 1024 elements

2021 ◽  
pp. 1-6
Author(s):  
Ken Iijima ◽  
Hajime Yokota ◽  
Toshio Yamaguchi ◽  
Masayuki Nakano ◽  
Takahiro Ouchi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Operating Characteristic ◽  
Focused Ultrasound ◽  
Characteristic Curve ◽  
Density Ratio ◽  
Predictive Ability ◽  
Curve Analysis ◽  
Regression Analyses ◽  
Multivariate Logistic Regression ◽  
Operating Characteristic Curve

OBJECTIVE Sufficient thermal increase capable of generating thermocoagulation is indispensable for an effective clinical outcome in patients undergoing magnetic resonance–guided focused ultrasound (MRgFUS). The skull density ratio (SDR) is one of the most dominant predictors of thermal increase prior to treatment. However, users currently rely only on the average SDR value (SDRmean) as a screening criterion, although some patients with low SDRmean values can achieve sufficient thermal increase. The present study aimed to examine the numerical distribution of SDR values across 1024 elements to identify more precise predictors of thermal increase during MRgFUS. METHODS The authors retrospectively analyzed the correlations between the skull parameters and the maximum temperature achieved during unilateral ventral intermediate nucleus thalamotomy with MRgFUS in a cohort of 55 patients. In addition, the numerical distribution of SDR values was quantified across 1024 elements by using the skewness, kurtosis, entropy, and uniformity of the SDR histogram. Next, the authors evaluated the correlation between the aforementioned indices and a peak temperature > 55°C by using univariate and multivariate logistic regression analyses. Receiver operating characteristic curve analysis was performed to compare the predictive ability of the indices. The diagnostic performance of significant factors was also assessed. RESULTS The SDR skewness (SDRskewness) was identified as a significant predictor of thermal increase in the univariate and multivariate logistic regression analyses (p < 0.001, p = 0.013). Moreover, the receiver operating characteristic curve analysis indicated that the SDRskewness exhibited a better predictive ability than the SDRmean, with area under the curve values of 0.847 and 0.784, respectively. CONCLUSIONS The SDRskewness is a more accurate predictor of thermal increase than the conventional SDRmean. The authors suggest setting the SDRskewness cutoff value to 0.68. SDRskewness may allow for the inclusion of treatable patients with essential tremor who would have been screened out based on the SDRmean exclusion criterion.

Middle Cerebral Artery-to-Uterine Artery Pulsatility Index Ratio Independently Predicts Adverse Perinatal Outcome in Pregnancies at Term

2020 ◽  
Author(s):  
Sufen Zhou ◽  
Hongyan Guo ◽  
Heng Liu ◽  
Mingqun Li
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Middle Cerebral Artery ◽  
Perinatal Outcome ◽  
Pulsatility Index ◽  
Uterine Artery ◽  
Regression Analyses ◽  
Multivariate Logistic Regression ◽  
Adverse Perinatal Outcome ◽  
Independent Risk Factors

Abstract Background: This study aimed to investigate potential predictors, including cerebroplacental ratio (CPR), middle cerebral artery (MCA)/uterine artery pulsatility index (PI) ratio, for adverse perinatal outcome in pregnancies at term.Methods: This was an observational, prospective study of recruited pregnancies at term. An adverse perinatal outcome was set as the primary observational endpoint. The receiver operating characteristic (ROC) curve was plotted to investigate the predictive and cut-off values of risk factors for adverse perinatal outcome. Independent risk factors (maternal, neonatal, prenatal ultrasound and Doppler variables) for adverse perinatal outcome were evaluated by the univariate and multivariate logistic regression analyses.Results: A total of 392 pregnancies at term were included and 19.4% of them had suffered adverse perinatal outcome. CPR (OR: 0.42, 95%CI: 0.20-0.93, P=0.032) and MCA/uterine artery PI ratio (OR: 0.25, 95%CI: 0.16-0.42, P=0.032) were two independent risk factors for adverse perinatal outcome by univariate and multivariate logistic regression analyses.Conclusions: MCA/uterine artery PI ratio is a good predictor of adverse perinatal outcome in pregnancies at term.

Physicians’ Choice in Using Internet and Fax for Patient Recruitment and Follow-up in a Randomized Controlled Trial

2004 ◽  
Vol 43 (03) ◽  
pp. 268-272 ◽  
Author(s):  
S. Morita ◽  
T. Fukui ◽  
J. Sakamoto ◽  
M. Rahman
Keyword(s):  
Randomized Controlled Trial ◽  
Odds Ratio ◽  
Controlled Trial ◽  
Data Entry ◽  
Regression Analyses ◽  
Multivariate Logistic Regression ◽  
Web Based ◽  
Randomized Controlled ◽  
The Web

Summary Objective: To examine the physicians’ preference between Web and fax-based remote data entry (RDE) system for an ongoing randomized controlled trial (RCT) in Japan. Methods: We conducted a survey among all the collaborating physicians (n = 512) of the CASE-J (Candesartan Antihypertensive Survival Evaluation in Japan) trial, who have been recruiting patients and sending follow-up data using the Web or a fax-based RDE system. The survey instrument assessed physicians’ choice between Web and fax-based RDE systems, their practice pattern, and attitudes towards these two modalities. Results: A total of 448 (87.5%) responses were received. The proportions of physicians who used Web, fax, and the combination of these two were 45.9%, 33.3% and 20.8%, respectively. Multivariate logistic regression analyses revealed that physicians 55 years or younger [odds ratio (OR) = 1.9, 95% confidence interval (CI) = 1.1-3.3] and regular users of computers (OR = 4.2, 95% CI = 2.1-8.2) were more likely to use the Web-based RDE system. Conclusions: This information would be useful in designing an RCT with a Web-based RDE system in Japan and abroad.

scholarly journals Allostatic Load, Unhealthy Behaviors, and Depressive Symptoms by Birthplace Among Older Adults in the Sacramento Area Latino Study on Aging (SALSA)

2019 ◽  
Vol 32 (7-8) ◽  
pp. 851-860 ◽  
Author(s):  
Erik J. Rodriquez ◽  
Melanie Sabado-Liwag ◽  
Eliseo J. Pérez-Stable ◽  
Anne Lee ◽  
Mary N. Haan ◽  
...  
Keyword(s):  
Older Adults ◽  
Logistic Regression ◽  
Depressive Symptoms ◽  
Allostatic Load ◽  
Risk Groups ◽  
Regression Analyses ◽  
Foreign Born ◽  
Count Range ◽  
The Relationship

Objective: To assess whether unhealthy behaviors moderated the relationship between allostatic load (AL) and future significant depressive symptoms (SDSs) among 1,789 older Latinos. Method: Longitudinal data included baseline AL, three unhealthy behaviors (UBs), and 2-year follow-up SDS. Multivariable logistic regression analyses, stratified by birthplace (U.S. vs. foreign born), modeled the effects of AL, UB count (range = 0-3), and their interaction on follow-up SDS. Results: Compared with U.S.-born, foreign-born participants engaged in fewer UBs (0.52 vs. 0.60 behaviors, p = .01) and had higher baseline SDS (31% vs. 20%, p < .001). Among foreign-born participants, the effect of AL on future SDS (adjusted odds ratios [aORs]; 95% confidence interval [CI]) significantly increased across UB counts of 0 to 3: 1.06 [0.83, 1.35], 1.46 [1.14, 1.87], 2.00 [1.18, 3.41], and 2.75 [1.18, 6.44], respectively. Discussion: Among foreign-born Latinos, these results were most pronounced for women and adults above age 80, which may represent higher risk groups requiring more intensive screening for depression.

scholarly journals Urinary neutrophil gelatinase-associated lipocalin values alone and combined with Prognostic Index predict septic AKI, DIC, and shock: a pilot study

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Yuichiro Shimoyama ◽  
Osamu Umegaki ◽  
Noriko Kadono ◽  
Toshiaki Minami
Keyword(s):  
Logistic Regression ◽  
Independent Predictor ◽  
Prognostic Index ◽  
Prognostic Scores ◽  
Regression Analyses ◽  
Multivariate Logistic Regression ◽  
Significant Independent Predictor ◽  
Neutrophil Gelatinase Associated Lipocalin ◽  
Life Threatening ◽  
Neutrophil Gelatinase

Abstract Objective Sepsis is a syndrome involving life-threatening organ dysfunction. The present study aimed to determine whether septic AKI, ARDS, DIC, and shock can be predicted more readily by combining uNGAL values and inflammation-based prognostic scores, over the use of uNGAL values alone. Results ROC curve analyses yielded the following cut-off values: AKI: 438.5 (ng/ml) for uNGAL at Day 1 (AUC, 0.8), 476.9 (ng/ml) for uNGAL at Day 2 (AUC, 0.86), 123.8 (ng/ml) for uNGAL at Day 3 (AUC, 0.81), 133.6 (ng/ml) for uNGAL at Day 4 (AUC, 0.78), 1.0 for iNS NGAL-NLR (AUC, 0.75), 2.0 for iNS NGAL-PI (AUC, 0.77), DIC; 648.5 (ng/ml) for uNGAL at Day 1 (AUC, 0.77); shock; 123.8 (ng/ml) for uNGAL at Day 3 (AUC, 0.71) and 9 for SOFA (AUC, 0.71). Multivariate logistic regression analyses revealed iNS NGAL-PI to be a significant independent predictor of AKI (OR, 20.62; 95% CI, 1.03–412.3; p = 0.048).

Genetic polymorphisms associated with clostridium difficile infection in multiple myeloma patients undergoing autologous stem cell transplantation.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 8522-8522
Author(s):  
Issam Hamadeh ◽  
Zainab Shahid ◽  
Manisha Bhutani ◽  
Jai Narendra Patel ◽  
Nury Steuerwald ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Stem Cell ◽  
Antibiotic Prophylaxis ◽  
Conditioning Regimen ◽  
High Dose ◽  
Host Immune System ◽  
Cell Transplant ◽  
Regression Analyses ◽  
Multivariate Logistic Regression ◽  
Buccal Swabs

8522 Background: CDI is the primary cause of infectious diarrhea in immunocompromised patients including those undergoing autologous stem cell transplant (SCT). Given the key role of gut microbiome and its interaction with host immune system, we investigated whether polymorphisms in innate immunity genes (identified through Ingenuity Pathway Analysis) were associated with CDI. Methods: We queried our database to identify MM patients who underwent an autologous SCT between April 2015-June 2019. Patients who had their buccal swabs collected through an IRB approved specimen collection protocol were included herein. Data were collected on age, conditioning regimen, CDI diagnosis, time from admission until CDI diagnosis, absolute neutrophil count (ANC) at time of CDI diagnosis, and antibiotic prophylaxis. Genomic DNA was extracted from buccal swabs and genotyped for 62 single nucleotide polymorphisms (SNPs) in ASPH , RLBP1L1, ATP7B, IL-8, FAK, TNFRSF14, CTH, TLR and IL-4. Univariate and multivariate logistic regression analyses were performed to assess association between CDI and presence of SNPs in these genes. Results: A total of 83 patients were identified (25 cases and 58 controls). Baseline characteristics were comparable between two groups. Median age was 67 years (range: 50-79). All patients received high dose melphalan as conditioning, and the same antibiotic prophylaxis during peri-transplant period. Median time from hospitalization until CDI diagnosis was 10 days (IQR:9 days), and median ANC was 0.7/mL (IQR:1.6/mL). Two SNPs (rs2227307 T > G in IL-8 and rs2234167 G > A in TNFRSF14) were significantly associated with CDI risk in both univariate and multivariate logistic regression analyses (Table). Conclusions: Our findings suggest that rs227307G (in IL-8) and rs2234167A (in TNFRSF14) alleles are potential risk factors for CDI after autologous SCT. Our findings, if validated in a larger cohort, would support genetic testing as a screening tool to identify patients who might benefit from prophylaxis against CDI. [Table: see text]

Musculoskeletal Problems in Relation to Farming, Gender and Smoking

2000 ◽  
Vol 44 (30) ◽  
pp. 5-588-5-590
Author(s):  
M G Björkstén ◽  
A. Rask-Andersen
Keyword(s):  
Logistic Regression ◽  
General Population ◽  
Smoking Habits ◽  
Control Group ◽  
Regression Analyses ◽  
Multivariate Logistic Regression ◽  
Male And Female ◽  
Musculoskeletal Problems ◽  
Female Farmers ◽  
Group Belonging

The aim of the present study was to investigate if smoking habits covariated with musculoskeletal problems among a group of male and female farmers and a control group from the general population. A questionnaire was sent to all farms and to a group of controls from the general population in the county of Uppsala. It comprised questions about smoking habits and musculoskeletal problems. Crosstabulations and multivariate logistic regression analyses were performed. In the analyses we included smoking habits, age, gender and group belonging, e g farmers or controls. Age did not give a higher risk for musculoskeletal problems in any of the groups. The results showed that problems were related both to gender and group combined or not combined with smoking habits.

scholarly journals The association between serum uric acid and controlled attenuation parameter in United States adults without hepatitis B and C: a cross-sectional study

2021 ◽  
Author(s):  
Mingming He ◽  
Lihong Chen ◽  
Yu Wang ◽  
Haijun Ma
Keyword(s):  
United States ◽  
Logistic Regression ◽  
Uric Acid ◽  
Hepatitis B ◽  
Positive Association ◽  
Regression Analyses ◽  
Multivariate Logistic Regression ◽  
Controlled Attenuation Parameter ◽  
Cross Sectional ◽  
The Relationship

Abstract Background: Controlled attenuation parameter (CAP) is a kind of widespread popular parameter to evaluate various types of hepatic steatosis by liver ultrasound transient elastography. We investigated the relationship between serum uric acid (SUA) and CAP without hepatitis B and C virus-infected in the United States adults, data from National Health and Nutrition Examination Survey (NHANES).Methods: The present study was cross-sectional research. 4319 American men and women participants ≥18 years old, without B and C hepatitis, were included in our analysis. There are some measures to evaluate the association between SUA and CAP by multivariate logistic regression analyses, fit smoothing curves, generalized additive models, two-piecewise linear regression model and subgroup analyses.Results: There was a positive association between the value of SUA and CAP by multivariate logistic regression analyses after adjusting for various confounders. Besides, the inflection point of non-linear curve relationship was identified as 4.3 mg/dL, for SUA <4.3 mg/dL, the effect size is 10.6 (P< 0.01); Correspondingly, SUA≥ 4.3 mg/dL, the effect size is 4.3 (P< 0.01).Finally, SUA was positively associated with glycohemoglobin less than 6.5% individuals (β =7.3, P< 0.01) and Fasting glucose less than 7.0 mmol/L individuals (β = 6.8, P< 0.01) in the subgroup analysis. Conclusions: Our research found the relationship between SUA and CAP is non-linear. Subgroup analysis indicated that the positive association between SUA and CAP were showed in non-diabetic patients but not in diabetic.

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