Googling "Anatomy dissection": Pre-pandemic, pandemic, and current scenario

OBJECTIVES

The present study was proposed to assess if the online search behavior for Anatomy dissection was affected by actual dissection being performed in the labs. The interest in dissection was ascertained and compared between pre-pandemic, pandemic, and current times.

METHODS

Online web search behavior for key terms related to "Anatomy dissection" was noticed worldwide using Google Trends Tool. Relative search volume (RSV) was downloaded for these keywords worldwide using all categories, and web search settings during a period from January 1, 2018, to July 31, 2022.

RESULTS

There was almost a similar google trend for the first three months in years 2018 and 2020, with a slightly lower RSV in March and a noticeable RSV decrease from April onwards in 2020. During the pre-pandemic period, the peak for RSV in the month of May was noticed. These peaks were missing in 2020 and 2021. In May 2022, RSV again increased. The top five countries with the highest search hit for "Anatomy Dissection" web were the Philippines, the United States, Canada, Australia, and India.

CONCLUSIONS

There was lower search trend for "Anatomy dissection" during the pandemic period as compared to pre-pandemic period. The search trends may be associated with the closure of dissection labs and it may be concluded that Anatomy learners search for this term more often, if they are actually performing the dissections. The future integrated multi-country data and analysis from different set-ups about their ways of dissection and labs before and after the pandemic may further clarify.

The effectiveness of prenatal and postnatal home visits by paramedical professionals and women's group meetings in improving maternal and child health outcomes in low and middle-income countries: a systematic review and meta-analysis

OBJECTIVE

To assess the effectiveness of prenatal and postnatal home visits (HVs) and women group meetings (WGMs) by paramedical professionals to improve maternal and child health outcomes in low- and middle-income countries (LMICs).

STUDY DESIGN

Systematic review and meta-analysis.

METHODS

We conducted a systematic review of trials published till December 2020, as per registered protocol in The International Prospective Register of Systematic Reviews (PROSPERO) (CRD42018091968). Outcomes were neonatal mortality rate (NMR), maternal mortality ratio (MMR), the incidence of low birth weight, and still birth rate (SBR). The Cochrane Pregnancy and Childbirth Group's Trials Register, Cochrane Central Register of Controlled Trials, PubMed, and Excerpta Medica Database (EMBASE) were searched. Pooled results were estimated using random-effects meta-analysis in RevMan version 5.2.

RESULTS

Twenty-five trials met the inclusion criteria. HVs were the key intervention in 12, WGMs in 11, and both interventions in 2 trials. The pooled estimates have shown that NMR was significantly reduced by HVs (OR 0.77, confidence interval [CI]: 0.67-0.90, P = 0.0007, I² = 77%) and WGMs (OR 0.76, CI: 0.65-0.90, P = 0.001, I² = 71%). SBR was significantly reduced by HVs (OR 0.77, CI: 0.70-0.85; P < 0.001, I² = 0%). Subgroup analysis of studies in which more than 10% of pregnant women participated in the WGMs showed significant reduction in NMR (OR 0.67, CI 0.58-0.77, P = 0.00001, I² = 31%) and MMR (OR 0.55, CI 0.36-0.84, P = 0.005, I² = 27%). Two studies reported improvement in birth weight by HVs.

CONCLUSIONS

HVs and WGMs (with >10% pregnant women) by paramedical professionals are effective strategies in reducing the NMR and MMR in LMICs. HVs were also effective in reducing SBR.

Prevalence and determinants of sexual abuse among adolescent girls during the COVID-19 lockdown and school closures in Ghana: A mixed method study

BACKGROUND

In the wake of the COVID-19 pandemic, concerns have been raised that the pandemic may derail global efforts against child sexual abuse (CSA).

OBJECTIVES

This study examines the prevalence and associated factors of sexual abuse among adolescent girls in the context of the COVID-19 pandemic in Ghana.

PARTICIPANTS AND SETTING

The sample comprised 853 adolescent girls aged 13-19 (16.03 ± 2.04 years) in Ghana.

METHODS

The study employed a concurrent mixed-method design.

RESULTS

Overall, the prevalence of CSA during the COVID-19 lockdown and school closures was 32.5 %. Protective factors for CSA were feeling safe in neighbourhood (AOR = 0.526, 95 % CI = [0.325, 0.850]) and parents often listen to opinions (AOR = 0.446, 95 % CI = [0.241, 0.826]). Risk factors for CSA were physical activity (AOR = 1.649, OR = 1.783, 95 % CI_AOR = [1.093, 2.487, 95 % CI_OR = [1.241, 2.561]), parents sometimes listen to opinions (AOR = 1.199, OR = 1.924, 95 % CI_AOR = [0.504, 2.853], 95 % CI_OR = [1.034, 3.582]), living with another relative (AOR = 2.352, OR = 2.484, 95 % CI_AOR = [0.270, 20.523], 95 % CI_OR = [0.317, 19.475]), Akan ethnicity (AOR = 1.576, OR = 1.437, 95 % CI_AOR = [0.307, 8.091], 95 % CI_OR = [0.316, 6.534]), having no disability (AOR = 1.099, OR = 1.138, 95 % CI_AOR = [0.679, 1.581], 95 % CI_OR = [0.786, 1.649]) and having a close relationship with parents (AOR = 1.334, OR = 1.752, 95 % CI_AOR = [0.746, 2.385], 95 % CI_OR = [1.096, 2.802]).

CONCLUSION

Knowledge of the risk and protective factors identified in this study can guide and inform the development of CSA prevention programmes during disruptive occurrences like school closures and lockdown.

Use of Machine Learning to Develop Prediction Models for Mortality and Stroke in Patients Undergoing Balloon Aortic Valvuloplasty

OBJECTIVE

To develop an artificial intelligence, machine learning prediction model for estimating in-hospital mortality and stroke in patients undergoing balloon aortic valvuloplasty (BAV).

METHODS

The National Inpatient Sample (NIS) database was used to identify patients who underwent BAV from 2005 to 2017. Outcomes analyzed were in-hospital all-cause mortality and stroke after BAV. Predictors of mortality and stroke were selected using LASSO regularization. A conventional logistic regression and a random forest machine learning algorithm were used to train the models for predicting outcomes. The performance of all the modeling algorithms for predicting in-hospital mortality and stroke was compared between models using c-statistic, F1 score, brier score loss, diagnostic accuracy, and Kolmogorov-Smirnov plots.

RESULTS

A total of 6962 patients with severe aortic stenosis who underwent BAV were identified. The performance of random forest classifier was comparable with logistic regression for predicting in-hospital mortality for all measures of performance (F1 score 0.422 vs 0.409, ROC-AUC 0.822 [95 % CI 0.787-0.855] vs 0.815 [95 % CI 0.779-0.849], diagnostic accuracy 70.42 % vs 70.93 %, KS-statistic 0.513 vs 0.494 and brier score loss 0.295 vs 0.291). The random forest algorithm significantly outperformed logistic regression in predicting in-hospital stroke with respect to all performance metrics: F1 score 0.225 vs 0.095, AUC 0.767 [0.662-0.858] vs 0.637 [0.499-0.754], brier score loss [0.399 vs 0.407], and KS-statistic [0.465 vs 0.254].

CONCLUSIONS

The good discrimination of machine learning models reveal the potential of artificial intelligence to improve patient risk stratification for BAV.

Machine learning models predict total charges and drivers of cost for transcatheter aortic valve replacement

Background

Given the increasing healthcare costs, there is an interest in developing machine learning (ML) prediction models for estimating hospitalization charges. We use ML algorithms to predict hospitalization charges for patients undergoing transfemoral transcatheter aortic valve replacement (TF-TAVR) utilizing the National Inpatient Sample (NIS) database.

Methods

Patients who underwent TF-TAVR from 2012 to 2016 were included in the study. The primary outcome was total hospitalization charges. Study dataset was divided into 80% training and 20% testing sets. We used following ML regression algorithms: random forest, gradient boosting, k-nearest neighbors (KNN), multi-layer perceptron and linear regression. ML algorithms were built for for 3 stages: Stage 1, including variables that were known pre-procedurally (prior to TF-TAVR); Stage 2, including variables that were known post-procedurally; Stage 3, including length of stay (LOS) in addition to the stage 2 variables.

Results

A total of 18,793 hospitalization for TF-TAVR were analyzed. The mean and median adjusted hospitalization charges were $220,725.2 ($137,675.1) and $187,212.0 ($137,971.0-264,824.8) respectively. Random forest regression algorithm outperformed other ML algorithms at all stages with higher R² score and lower mean absolute error (MAE), root mean squared area (RMSE) and root mean squared logarithmic error (RMSLE) (Stage 1: MAE 79,979.11, R² 0.157; Stage 2: MAE 76,200.09, R² 0.256; Stage 3: MAE 69,350.09, R² 0.453). LOS was the most important predictor of hospitalization charges.

Conclusions

We built ML algorithms that predict hospitalization charges with good accuracy in patients undergoing TF-TAVR at different stages of hospitalization and that can be used by healthcare providers to better understand the drivers of charges.

Temporal Trends in the Utilization and Outcomes of Balloon Aortic Valvuloplasty in the Pre-Transcatheter Aortic Valve Implantation (TAVI) and TAVI Eras

We used a nationwide cohort to (1) compare characteristics and outcomes of patients who underwent balloon aortic valvuloplasty (BAV) in pre-transcatheter aortic valve transplantation (TAVI) and TAVI eras, (2) examine trends in utilization and outcomes of BAV from 2005 to 2017, (3) assess the association of BAV procedural volume with hospital-based TAVI volume, and (4) understand trends and outcomes of BAV in TAVI and non-TAVI centers in the TAVI era. Pre-TAVI era included hospitalizations from 2005 to 2011, and TAVI era included hospitalizations from 2012 to 2017. In the TAVI era, hospitals were classified into quartiles based on the number of TAVI procedures performed. Trends in volume of BAV procedures from 2012 to 2017 were assessed in non-TAVI and TAVI centers (based on TAVI volume). Between 2005 and 2017, a total of 6,962 hospitalizations for BAV were identified. There were no significant differences in in-hospital mortality or stroke between pre-TAVI and TAVI eras (mortality: pre-TAVI, 8.5% vs TAVI era, 9.3%, p = 0.354; stroke: pre-TAVI, 1.9% vs TAVI era, 1.3%, p = 0.083). However, acute kidney injury was more prevalent in the TAVI era and blood transfusion in the pre-TAVI era. Importantly, patients who underwent BAV in the TAVI era were more likely to have a greater number of co-morbidities and to undergo nonelective procedures. From 2005 to 2017, there was 10-fold increase in utilization of BAV. In the TAVI era, the maximum increase in number of BAV procedures was seen in hospitals with highest TAVI volume. In conclusion, although BAV procedural volume increased approximately 10-fold between 2005 and 2017, with concomitant expansion of TAVI, rates of mortality and stroke have remained stable. Despite this, the rate of BAV utilization continues to increase, thereby indicating a significant opportunity to improve outcomes in this patient population.

Autoimmune connective tissue diseases and aortic valve replacement outcomes: a population-based study

Aims

Patients with autoimmune connective tissue diseases (CTDs) have a high burden of valvular heart disease and are often thought of as high surgical risk patients.

Methods and results

Patients undergoing aortic valve replacement (AVR) were identified in the Nationwide Readmissions Database between January 2012 and December 2018. Patients with a history of systemic lupus erythematosus, rheumatoid arthritis, systemic sclerosis, mixed C, Sjögren syndrome, polymyositis, and dermatomyositis were included in the CTD cohort. Patients undergoing coronary artery bypass grafting concomitantly with AVR were excluded. A total of 569 600 hospitalizations were included, of which16 531 (2.9%) had CTD. CTD patients were more likely to be females, with higher rates of heart failure, pulmonary hypertension, and more likely to be insured by Medicare. CTD patients had lower mortality than non-CTD patients [odds ratio (OR) 0.66; 95% confidence interval (CI): 0.59–0.74] and stroke [OR 0.87; 95% (CI): 0.79–0.97]. CTD patients undergoing SAVR had lower mortality [OR 0.69; 95% (CI): 0.60–0.80] and stroke [OR 0.86; 95% (CI): 0.75–0.98). CTD patients undergoing TAVR had lower mortality outcomes [OR 0.67; 95% (CI): 0.56–0.80]; however, they had comparable stroke outcomes [OR 0.97; 95% (CI): 0.83–1.13, P = 0.69].

Conclusions

Outcomes for patients with CTD requiring AVR are not inferior to their non-CTD counterparts. A comprehensive heart team selection of patients undergoing AVR approaches should place CTD history under consideration; however, pre-existing CTD should not be prohibitive of AVR interventions.

Postdischarge-to-30-Day Mortality Among Patients Receiving MitraClip: A Systematic Review and Meta-Analysis

Background

MitraClip (MC) implantation is the recommended treatment for severe symptomatic mitral regurgitation in patients not responding to medical therapy and at prohibitive surgical risk. It is important to quantify immediate mortality during postdischarge-to-30-day period so as to improve the procedural outcomes. Hence, we aim to identify the incidence of postdischarge-to-30-day mortality and its associated predictors using the technique of meta-analysis.

Methods

We searched Medline, Embase, and Cochrane CENTRAL databases from inception until July 3, 2019 for studies reporting mortality prior to discharge, at 30 days and 1 year after MC implantation. The primary outcome was postdischarge-to-30-day all-cause mortality.

Results

Of 2394 references, 15 studies enrolling 7498 patients were included. Random effects analysis showed that all-cause cumulative inpatient, 30-day, and 1-year mortality was 2.40% (2.08, 2.77; I² = 0%), 4.31% (3.64, 5.09, I² = 41.9%), and 20.71% (18.32; 23.33, I² = 81.5%), respectively. The postdischarge-to-30-day mortality was 1.70% (95% confidence interval: 1.0, 2.70; I² = 84%). A total of 71.50% of deaths (95% confidence interval: 36.80-91.50, I² = 63%) in the postdischarge-to-30-day period were due to cardiac etiology. On meta-regression, pre-MC left ventricular ejection fraction (p = 0.003), Log.Euroscore (p = 0.047), Society of Thoracic Surgeons Predicted Risk of Mortality (p < 0.001), and prolonged ventilation >48 ​hours (p < 0.001) were found to be its significant predictors.

Conclusions

Our meta-analysis reports an additional mortality of ∼2% immediately after MC implantation during the postdischarge-to-30-day period. Majority of deaths occurred due to cardiac causes. Pre-MC left ventricular ejection fraction, Log.Euroscore, Society of Thoracic Surgeons Predicted Risk of Mortality score, and prolonged ventilation were found to be its significant predictors. Further studies are needed to better understand the causes of this early mortality to maximize benefits of this important therapy.