Predicting the individual probability of macular hole closure following intravitreal ocriplasmin injections for vitreomacular traction release using baseline characteristics

The primary objective was to create and establish a new formula that predicts the individual probability of macular hole closure for eyes with full thickness macular holes (FTMH) accompanied by vitreomacular traction (VMT) which received enzymatic vitreolysis using intravitreally administered ocriplasmin. The secondary objective was to evaluate the forecast reliability of a previously published formula for VMT resolution in VMT-only eyes (OddsIVO-Success = eIntercept × ORyears × ORln(µm); ProbabilityIVO-Success = OddsIVO-Success/(OddsIVO-Success + 1)) on VMT resolution using the current dataset of eyes with FTMH accompanied by VMT. Retrospective analysis of the OASIS, ORBIT, and INJECT-studies. Patients with FTMH and VMT with complete information (n = 213) were included. The effect of gender, age, FTMH diameter, lens status and the presence of epiretinal membranes (ERM) on FTMH closure was assessed using separate univariate logistic regression analyses. With regard to VMT release separate univariate regression analyses were carried out and results were compared with formerly published data of VMT resolution in eyes with VMT only. Overall, 126 eyes (63%) experienced VMT resolution within 28 days. Younger age (p < 0.0001) and VMT diameter (p = 0.041) had a significant impact on VMT release. Overall, 81 eyes (38%) treated with ocriplasmin showed FTMH closure within 28 days. Univariate analysis of the different predictors analyzed revealed that FTMH diameter < 250 µm had a significant impact on treatment success (p = 0.0495). It was not possible to calculate and establish a new multivariate formula that can predict the individual FTMH closure probability for eyes with FTMHs and VMT. However, the results of VMT release prediction in eyes with FTMHs accompanied by VMT matched the prediction of VMT release in eyes with VMT only when using the previously published formula. All in all, predictors for calculating the individual probability of VMT resolution on the one hand and FTMH closure on the other hand are different suggesting diverse pathophysiological mechanisms.

Factors Predicting Successful Vaginal Delivery Following Induction of Labor in Term Pregnancy v1

Objective This study was proposed to evaluatefactors predicting successful vaginal delivery following labor induction and develop induction prediction model in term pregnancy among Thai pregnant women. Method We conducted a retrospective cohort study using electronic medical recordsof 23,833 deliveries from April 2010 - July 2021 at tertiary-level hospital in Bangkok, Thailand. Univariate regression was performed to identify association of individual parameters to successful vaginal delivery. Multiple logistic regression analysis of all possible variables from univariate analysis was performed to develop prediction model with statistically significant of p value < 0.05. Results Of thetotal 809 labor-induced pregnancies, the vaginal delivery rate was 56.6%. Among predicting variables, history of previous vaginal delivery (aOR 5.75, 95%CI3.701-8.961), maternal delivery BMI < 25 kg/m2 (aOR 2.010, 95%CI1.303-3.286), estimated fetal weight < 3500 g (aOR 2.193, 95%CI1.246-3.860), and gestational age ≤ 39 weeks (aOR 1.501, 95%CI1.038-2.173) significantly increased the probability of successful vaginal delivery following labor induction. The final prediction model has been internally validated. Model calibration and discrimination were satisfactory with Hosmer-Lemeshow test P= 0.21 and with AUC of 0.732 (95% CI 0.692-0.772). Conclusions This study determined the pragmatic predictors for successful vaginal delivery following labor induction comprised of history of previous vaginal delivery, maternal delivery BMI < 25 kg/m2, estimated fetal weight < 3500 g, and gestational age ≤ 39 weeks. The final induction prediction model was well-performing internally validated prediction model to estimate individual probability when undergoing induction of labor. Despite of restricted population, the predicting factors and model could be useful for further prospective study and clinical practice to improve induction outcomes.

A New Prognostic Algorithm Predicting HCC Recurrence in Patients With Barcelona Clinic Liver Cancer Stage B Who Received PA-TACE

BackgroundPostoperative adjuvant transcatheter arterial chemoembolization (PA-TACE) is effective in preventing the recurrence of hepatocellular carcinoma (HCC) in patients treated with surgery. However, there is a lack of reports studying the risk factors associated with recurrence in HCC patients who received PA-TACE. In this study, we identified the independent risk factors for recurrence of HCC patients who received PA-TACE. We also developed a novel, effective, and valid nomogram to predict the individual probability of recurrence, 1, 3, and 5 years after PA-TACE.MethodsA retrospective study was performed to identify the independent risk factors for recurrence of HCC in a group of 502 patients diagnosed in stage B based on the Barcelona Clinic Liver Cancer (BCLC) evaluation system for HCC that underwent curative resections. Then, subgroup analysis was performed for 184 patients who received PA-TACE, who were included in the training cohort. The other 147 HCC patients were included in a validation cohort. A recurrence-free survival (RFS)-predicting nomogram was constructed, and results were assessed using calibration and decision curves and a time-dependent AUC diagram.ResultsPA-TACE was shown to be a significant independent prognostic value for patients with BCLC stage B [p &lt; 0.001, hazard ratio (HR) = 0.508, 95% CI = 0.375–0.689 for OS, p = 0.002; HR = 0.670, 95%CI = 0.517–0.868 for RFS]. Alpha fetoprotein (AFP), tumor number, tumor size, microvascular invasion (MVI), and differentiation were considered as independent risk factors for RFS in the training cohort, and these were further confirmed in the validation cohort. Next, a nomogram was constructed to predict RFS. The C-index for RFS in the nomogram was 0.721 (95% CI = 0.718–0.724), which was higher than SNACOR, HAP, and CHIP scores (0.587, 0.573, and 0.607, respectively). Calibration and decision curve analyses and a time-dependent AUC diagram were used. Our nomogram showed stronger performance than these other nomograms in both the training and validation cohorts.ConclusionsHCC patients diagnosed as stage B according to BCLC may benefit from PA-TACE after surgery. The RFS nomogram presented here provides an accurate and reliable prognostic model to monitor recurrence. Patients with a high recurrence score based on the nomogram should receive additional high-end imaging exams and shorter timeframes in between follow-up visits.

A nomogram for predicting postoperative pulmonary infection in esophageal cancer patients

Background Although postoperative pulmonary infection (POI) commonly occurs in patients with esophageal cancer after curative surgery, a patient-specific predictive model is still lacking. The main aim of this study is to construct and validate a nomogram for estimating the risk of POI by investigating how perioperative features contribute to POI. Methods This cohort study enrolled 637 patients with esophageal cancer. Perioperative information on participants was collected to develop and validate a nomogram for predicting postoperative pulmonary infection in esophageal cancer. Predictive accuracy, discriminatory capability, and clinical usefulness were evaluated by calibration curves, concordance index (C-index), and decision curve analysis (DCA). Results Multivariable logistic regression analysis indicated that length of stay, albumin, intraoperative bleeding, and perioperative blood transfusion were independent predictors of POI. The nomogram for assessing individual risk of POI indicated good predictive accuracy in the primary cohort (C-index, 0.802) and validation cohort (C-index, 0.763). Good consistency between predicted risk and observed actual risk was presented as the calibration curve. The nomogram for estimating POI of esophageal cancer had superior net benefit with a wide range of threshold probabilities (4–81%). Conclusions The present study provided a nomogram developed with perioperative features to assess the individual probability of infection may conducive to strengthen awareness of infection control and provide appropriate resources to manage patients at high risk following esophagectomy.

Development and Validation of a Model to Predict Severe Hospital-Acquired Acute Kidney Injury in Non-Critically Ill Patients

Background. The current models developed to predict hospital-acquired AKI (HA-AKI) in non-critically ill fail to identify the patients at risk of severe HA-AKI stage 3. Objective. To develop and externally validate a model to predict the individual probability of developing HA-AKI stage 3 through the integration of electronic health databases. Methods. Study set: 165,893 non-critically ill hospitalized patients. Using stepwise logistic regression analyses, including demography, chronic comorbidities, and exposure to risk factors prior to AKI detection, we developed a multivariate model to predict HA-AKI stage 3. This model was then externally validated in 43,569 non-critical patients admitted to the validation center. Results. The incidence of HA-AKI stage 3 in the study set was 0.6%. Among chronic comorbidities, the highest odds ratios were conferred by ischemic heart disease, ischemic cerebrovascular disease, chronic congestive heart failure, chronic obstructive pulmonary disease, chronic kidney disease and liver disease. Among acute complications, the highest odd ratios were associated with acute respiratory failure, major surgery and exposure to nephrotoxic drugs. The model showed an AUC of 0.906 (95% CI 0.904 to 0.908), a sensitivity of 89.1 (95% CI 87.0–91.0) and a specificity of 80.5 (95% CI 80.2–80.7) to predict HA-AKI stage 3, but tended to overestimate the risk at low-risk categories with an adequate goodness-of-fit for all risk categories (Chi2: 16.4, p: 0.034). In the validation set, incidence of HA-AKI stage 3 was 0.62%. The model showed an AUC of 0.861 (95% CI 0.859–0.863), a sensitivity of 83.0 (95% CI 80.5–85.3) and a specificity of 76.5 (95% CI 76.2–76.8) to predict HA-AKI stage 3 with an adequate goodness of fit for all risk categories (Chi2: 15.42, p: 0.052). Conclusions. Our study provides a model that can be used in clinical practice to obtain an accurate dynamic assessment of the individual risk of HA-AKI stage 3 along the hospital stay period in non-critically ill patients.

A Suggestion for Hip Replacement Surgery: Probabilistic Surveillance

Objective: Surgical site infection (SSI) surveillance is time-consuming and hard. Identifying high-risk patients and focusing on these patients will be cost and time effective. This study aims to develop a model to identify high-risk patients for the development of SSI after hip replacement surgery and to estimate the utility of the model. Methods: Logistic regression model was created to determine the risk of SSI development using the National Health Service Associated Surveillance Network (USHİİSA) data. The stability of the model was tested using the Bootstrap resampling method. The individual probability of developing SSI was determined for each patient by using the model. The threshold probability to be used in distinguishing high-risk patients was found 1.2% by ROC analysis. For hospitals with different SSI rates and surveillance sensitivity, the utility of the model has been estimated by various parameters. Results: Female gender (OR:1.52; 95% CI:1.22-1.88), being over 65 years of age (OR:2.06; 95% CI:1.63-2.62), procedure duration longer than 75th percentile (OR:1.32; 95% CI:1.07-1.63), ASA score over 3 (OR:2.10; 95% CI:1.48-2.99), and surgery performed in a hospital other than a private hospital (p<0.001) were found to be independent risk factors for the development of SSI. When focusing on high-risk patients, as the rate of SSI of a hospital increases, the number of patients that need to be focused on detecting one more SSI decreased, and the number of additional SSIs increased. As the surveillance sensitivity of the hospitals decreases, the new rate obtained differs more from the old rate. Conclusions: Focusing on high-risk patients identified using the model caused to eliminate approximately half of the patients, thus saving labor and time. Using this model can be particularly beneficial for hospitals with a high SSI burden and low surveillance capacity. The model can be integrated into the national surveillance system so that high-risk patients can be prioritized. Modeling may be considered for the other surgeries.

Development and Validation of a Clinical Pregnancy Failure Prediction Model for Poor Ovarian Responders During IVF/ICSI

BackgroundsDespite the great advances in assisted reproductive technology (ART), poor ovarian response (POR) is still one of the most challenging tasks in reproductive medicine. This predictive model we developed aims to predict the individual probability of clinical pregnancy failure for poor ovarian responders (PORs) under in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI).MethodsThe nomogram was developed in 281 patients with POR according to the Bologna criteria from January 2016 to December 2019, with 179 in the training group and 102 in the validation group. Univariate and multivariate logistic regression analyses were used to identify characteristics that were associated with clinical pregnancy failure. The nomogram was constructed based on regression coefficients. Performance was evaluated using both calibration and discrimination.ResultsAge &gt;35 years, body mass index (BMI) &gt;24 kg/m2, basic follicle-stimulating hormone (FSH) &gt;10 mIU/ml, basic E2 &gt;60 pg/ml, type B or C of endometrium on human chorionic gonadotropin (hCG) day, and the number of high-quality embryos &lt;2 were associated with pregnancy failure of POR patients. The area under the receiver operating characteristic curve (AUC) of the training set is 0.786 (95% confidence interval (CI): 0.710–0.861), and AUC in the validation set is 0.748 (95% CI: 0.668–0.827), showing a satisfactory goodness of fit and discrimination ability in this nomogram.ConclusionOur nomogram can predict the probability of clinical pregnancy failure in PORs before embryo transfer in IVF/ICSI procedure, to help practitioners make appropriate clinical decisions and to help infertile couples manage their expectations.

Impact of essential workers in the context of social distancing for epidemic control

New emerging infectious diseases are identified every year, a subset of which become global pandemics like COVID-19. In the case of COVID-19, many governments have responded to the ongoing pandemic by imposing social policies that restrict contacts outside of the home, resulting in a large fraction of the workforce either working from home or not working. To ensure essential services, however, a substantial number of workers are not subject to these limitations, and maintain many of their pre-intervention contacts. To explore how contacts among such “essential” workers, and between essential workers and the rest of the population, impact disease risk and the effectiveness of pandemic control, we evaluated several mathematical models of essential worker contacts within a standard epidemiology framework. The models were designed to correspond to key characteristics of cashiers, factory employees, and healthcare workers. We find in all three models that essential workers are at substantially elevated risk of infection compared to the rest of the population, as has been documented, and that increasing the numbers of essential workers necessitates the imposition of more stringent controls on contacts among the rest of the population to manage the pandemic. Importantly, however, different archetypes of essential workers differ in both their individual probability of infection and impact on the broader pandemic dynamics, highlighting the need to understand and target intervention for the specific risks faced by different groups of essential workers. These findings, especially in light of the massive human costs of the current COVID-19 pandemic, indicate that contingency plans for future epidemics should account for the impacts of essential workers on disease spread.

Identifying and alleviating bias due to differential depletion of susceptible people in post-marketing evaluations of COVID-19 vaccines

Recent studies have provided key information about SARS-CoV-2 vaccines' efficacy and effectiveness (VE). One important question that remains is whether the protection conferred by vaccines wanes over time. However, estimates over time are subject to bias from differential depletion of susceptibles between vaccinated and unvaccinated groups. Here we examine the extent to which biases occur under different scenarios and assess whether serologic testing has the potential to correct this bias. By identifying non-vaccine antibodies, these tests could identify individuals with prior infection. We find that the main determinant of bias is the proportion of the population that has been infected since vaccination began, which is influenced by several factors. In scenarios with high baseline VE, differential depletion of susceptibles creates minimal bias in VE estimates. However, if VE is lower, the bias for leaky vaccines (that reduce individual probability of infection given contact) is larger and should be corrected if the mechanism is known to be leaky. Conducting analyses both unadjusted and adjusted for past infection could give lower and upper bounds for the true VE. Studies of VE should therefore enroll individuals regardless of prior infection history but also collect information, ideally through serologic testing, on this critical variable.