30-day Death Research Articles

Using Quality Control Charts for Monitoring COVID-19 Daily Cases and Deaths in Türkiye

The national partial curfews in Turkey, announced after normal time period in 2020’s summer, began on 18.11.2020. In this study, for the periods between 25.11.2020-31.05.2022, the daily new cases and for the periods between 27.03.2020-31.05.2022 deaths and fatality rates are monitored. In this research, Run charts, control charts of c and p are used for monitoring the COVID-19 process in Turkey. In the periods of national extended curfews (December 1, 2020–February 28, 2021), full lockdown (April 29–May 16, 2021) and gradual normalization (May 17– May 31, 2021) the number of daily cases and deaths show downward trend as it is expected. However, in the periods of local decision-making phase (March 1–29, 2021) and revised local decision-making phase (March 30–April 13, 2021) the number of new daily cases and deaths show upward trend. For the latest period between June 1, 2021-May 31, 2022, in winter season the number of deaths and daily cases increasing as it is expected then in spring season they decrease. The daily new cases and deaths due to COVID-19 monitored rapidly and effectively by control charts. It is obvious that some of the public did not internalize the negative impacts of the virus in 2020 and consequently disregarded health guidelines in 2021.

Deploying ADVISER: Impact and Lessons from Using Artificial Intelligence for Child Vaccination Uptake in Nigeria

More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring in underdeveloped countries with low vaccination uptake. One of the United Nations' sustainable development goals (SDG 3) aims to end preventable deaths of newborns and children under five years of age. We focus on Nigeria, where the rate of infant mortality is appalling. In particular, low vaccination uptake in Nigeria is a major driver of more than 2,000 daily deaths of children under the age of five years. In this paper, we describe our collaboration with government partners in Nigeria to deploy ADVISER: AI-Driven Vaccination Intervention Optimiser. The framework, based on an integer linear program that seeks to maximize the cumulative probability of successful vaccination, is the first successful deployment of an AI-enabled toolchain for optimizing the allocation of health interventions in Nigeria. In this paper, we provide a background of the ADVISER framework and present results, lessons, and success stories of deploying ADVISER to more than 13,000 families in the state of Oyo, Nigeria.

Improving cervical cancer classification in PAP smear images with enhanced segmentation and deep progressive learning-based techniques.

Cervical cancer, a prevalent and deadly disease among women, comes second only to breast cancer, with over 700 daily deaths. The Pap smear test is a widely utilized screening method for detecting cervical cancer in its early stages. However, this manual screening process is prone to a high rate of false-positive outcomes because of human errors. Researchers are using machine learning and deep learning in computer-aided diagnostic tools to address this issue. These tools automatically analyze and sort cervical cytology and colposcopy images, improving the precision of identifying various stages of cervical cancer. This article uses state-of-the-art deep learning methods, such as ResNet-50 for categorizing cervical cancer cells to assist medical professionals. The method includes three key steps: preprocessing, segmentation using k-means clustering, and classifying cancer cells. The model is assessed based on performance metrics viz; precision, accuracy, kappa score, precision, sensitivity, and specificity. In the end, the high success rate shows that the ResNet50 model is a valuable tool for timely detection of cervical cancer. In conclusion, the infected cervical region is pinpointed using spatial K-means clustering and preprocessing operations. This sequence of actions is followed by a progressive learning technique. The Progressive Learning technique then proceeded through several stages: Stage 1 with 64 × 64 images, Stage 2 with 224 × 224 images, Stage 3 with 512 × 512 images, and the final Stage 4 with 1024 × 1024 images. The outcomes show that the suggested model is effective for analyzing Pap smear tests, achieving 97.4% accuracy and approx. 98% kappa score.

CAUSAL ROLES OF SERUM URIC ACID LEVELS AND GOUT IN SEPSIS: A MENDELIAN RANDOMIZATION STUDY.

Objective: Several epidemiological studies have identified a potential link between serum uric acid (UA), gout, and sepsis. The primary objective of this study is to delve deeper into this connection, investigating the causal effect of UA and gout on sepsis by applying Mendelian randomization (MR). Methods: The causal relationship was analyzed using data from Genome-Wide Association Study (GWAS). Inverse variance weighting (IVW) was used as the main analysis method. Three complementary methods were used for our MR analysis, which included the MR-Egger regression method, the weighted median method, the simple median method. Horizontal pleiotropy was identified by MR-Egger intercept test. Cochran's Q statistics were employed to assess the existence of instrument heterogeneity. The leave-one-out method was used as a sensitivity analysis. Results: The IVW results indicated that there was a positive causal relationship between UA and sepsis (critical care) (odds ratio [OR] = 0.24, 95% confidence interval [CI]: 0.04 to 0.43, P = 0.018, F = 4,291.20). There was no significant association between UA and sepsis (28-day death in critical care) (OR = 0.10, 95% CI = -0.29 to 0.50, P = 0.604). There was no significant association between gout and sepsis (critical care) (OR = 0.85, 95% CI = -4.87 to 6.57, P = 0.771), and sepsis (28-day death in critical care) (OR = -6.30, 95% CI = -17.41 to 4.81, P = 0.267). Horizontal pleiotropy was absent in this study. The results were robust under all sensitivity analyses. Conclusion: The study revealed that elevated UA levels were causally linked with sepsis (critical care). No causal relationship had been found between UA and sepsis (28-day death in critical care), as well as between gout and sepsis.

Clinical outcomes of the Myval transcatheter heart valve system in patients with severe aortic valve stenosis: a two-year follow-up observational study.

Limited data exist on long-term follow-up of severe aortic stenosis (SAS) patients who have undergone transcatheter aortic valve implantation (TAVI) with a new generation, balloon expandable Myval transcatheter heart valve (THV). Thus, we sought to investigate the performance and 2-year clinical outcome of the Myval THV system based on Valve Academic Research Consortium-3 (VARC-3) criteria. A multi-centre, registry-based, observational study was conducted, which included 207 consecutive degenerative SAS patients, from Turkey (n = 128), Italy (n = 58), and Greece (n = 21) (mean [standard deviation] 81 (7) years, 94 [45%] men; 73% NYHA III or IV; EuroSCORE II 5.2% [2.4%]); all patients underwent TAVI with Myval. Patients were followed up at 1 year and 2 years after implantation. Clinical and procedural outcomes were defined according to VARC-3 criteria. Technical success was observed in 204 (99%), device success was observed in 189 (91%), early safety was observed in 161 (78%), and clinical efficacy was observed in 163 (79%) patients. The 30-day death rate was 7.7%; of these, 3.4% were due to cardiovascular reasons. All-cause and cardiovascular mortality rates were 9.7% and 4.3% at 1-year follow-up, and 17.4% and 9.7% at 2-year follow-up, respectively. Incidence of ≥ moderate paravalvular leak (PVL) at 30 days, 1 year and 2 years of follow-up were 3.4%, 4.3% and 4.8%. A total of 11.1% of patients required a permanent pacemaker implantation (PPI) at 30 days after implantation, while the cumulative rate of PPI at 2 years was 12.1%. In this cohort of patients with SAS, the Myval was found to be safe and effective in up to 2 years of follow-up.

Blood pressure and intensive treatment up-titration after acute heart failure hospitalization: Insights from the STRONG-HF trial.

A high-intensity care (HIC) strategy with rapid guideline-directed medical therapy (GDMT) up-titration and close follow-up visits improved outcomes, compared to usual care (UC), in patients recently hospitalized for acute heart failure (AHF). Hypotension is a major limitation to GDMT implementation. We aimed to assess the impact of baseline systolic blood pressure (SBP) on the effects of HIC versus UC and the role of early SBP changes in STRONG-HF. A total of 1075 patients hospitalized for AHF with SBP ≥100 mmHg were included in STRONG-HF. For the purpose of this post-hoc analysis, patients were stratified by tertiles of baseline SBP (<118, 118-128, and ≥129 mmHg) and, in the HIC arm, by tertiles of changes in SBP from the values measured before discharge to those measured at 1 week after discharge (≥2 mmHg increase, ≤7 mmHg decrease to <2 mmHg increase, and ≥8 mmHg decrease). The primary endpoint was 180-day heart failure rehospitalization or death. The effect of HIC versus UC on the primary endpoint was independent of baseline SBP evaluated as tertiles (pinteraction = 0.77) or as a continuous variable (pinteraction = 0.91). In the HIC arm, patients with increased, stable and decreased SBP at 1 week reached 83.5%, 76.2% and 75.3% of target doses of GDMT at day 90. The risk of the primary endpoint was not significantly different between patients with different SBP changes at 1 week (adjusted p = 0.46). In STRONG-HF, the benefits of HIC versus UC were independent of baseline SBP. Rapid GDMT up-titration was performed also in patients with an early SBP drop, resulting in similar 180-day outcome as compared to patients with stable or increased SBP.

Benford’s law analysis to evaluate the quality data of COVID-19 epidemiological surveillance in Indonesia

Countries worldwide, including Indonesia, grappled with the unprecedented challenges brought about by the coronavirus disease (COVID-19) pandemic. Surveillance data vividly illustrates the profound effect of the COVID-19 pandemic in Indonesia. Both daily cases and deaths were raised, revealing the rapid transmission of the virus within communities. A quantitative study using a statistical approach was accomplished with secondary data to evaluate the quality of COVID-19 epidemiological surveillance data in Indonesia during the period between March 2020 to January 2021. The data was sourced from the World Health Organization (WHO) website using data reports on COVID-19 confirmed cases and deaths. A rapid tool called the first digit law or the fulfillment of Benford’s law was used to suggest good data quality for epidemiological surveillance. Data analysis used the Chi-squared test and the log-likelihood ratio test. Also, it displayed the difference in mean absolute deviation (MAD) to identify the proximity of the data and Benford’s law distribution. The results showed that both confirmed, and death case distributions were statistically non-conformity with Benford’s law distribution. In terms of quality data regarding the COVID-19 pandemic, the epidemiological surveillance system falls short of Benford’s law assumption. Benford's law has been acknowledged as an initial analysis that can expeditiously assess the performance of a surveillance system. The next phase of this study would be to conduct a complete evaluation suitably, especially in post-pandemic COVID-19.

Effect of neutrophil to lymphocyte ratio on prognosis of elderly patients with severe sepsis combined with diabetes mellitus

BackgroundTo investigate the predictive value of neutrophil-to-lymphocyte ratio (NLR) in the short-term prognosis of elderly patients with severe sepsis combined with diabetes mellitus (DM).MethodsThe clinical data of 162 elderly patients with severe sepsis combined with DM from January 2018 to December 2022 were retrospectively collected. These patients were divided into a survival group (n = 104) and a death group (n = 58) according to 90-day prognosis. The number of neutrophils, lymphocytes, and NLR were compared. The optimal cut-off value for NLR to predict 90-day prognosis in elderly patients with severe sepsis combined with DM was determined using Receiver Operator Characteristic (ROC) curves, and the patients were divided into high and low NLR groups depending on the optimal cut-off value. The Kaplan-Meier method was used to plot the survival curves of the high and low NLR groups. Risk factors for the 90-day death in elderly patients with severe sepsis combined with DM were analyzed by a multivariate cox regression model.ResultsThere were no significant differences in gender, age, history of hypertension and hyperlipidemia, intensive care unit (ICU) stay, duration of mechanical ventilation, and oxygenation index between the survival group and death group (p > 0.05). However, acute physiological and chronic health evaluation II (APACHE II) scores, and sepsis-related organ failure assessment (SOFA) scores were significantly lower in the survival group compared with the death group (p < 0.05). In the survival group, neutrophils counts and NLR were much lower than those in the death group, while lymphocytes counts were much higher (p < 0.05). ROC curves showed that the optimal cut-off value for NLR to predict 90-day mortality in elderly patients with severe sepsis combined with DM was 3.482. Patients were divided into high NLR and low NLR groups based on whether NLR was ≥ 3.482. In terms of the log-rank test results, patients in the low NLR group had a significantly higher 90-day survival rate than those in the high NLR group (Logrank χ2 = 8.635, p = 0.003). The multivariate cox regression model showed that the length of ICU stay longer than 15 days and NLR ≥ 3.482 were independent risk factors for 90-day prognosis in elderly patients with severe sepsis combined with DM.ConclusionNLR ≥ 3.482 can be used to predict whether poor prognosis occurs in the short term after illness in elderly patients with severe sepsis combined with DM, and has good assessment value.

The threaten of typhoons to the health of residents in inland areas: a study on the vulnerability of residents to death risk during typhoon “Lekima”

BackgroundStudies had suggested increased risk of death of residents was associated with typhoons, particularly coastal regions. However, these findings ignored the impact of inland typhoons on the health of residents, especially the indirect death risk caused by typhoons. This study aimed to investigate the acute death risk of residents during inland typhoon Lekima in Jinan, further identify vulnerable populations and areas.MethodsWe selected the daily death from 11 to 27th August 2019 in Jinan as case period, and conducted a time-stratified case-crossover design to match the contemporaneous data from 2016 to 2018 as control period. We used the generalized linear Poisson models to estimate the related effects of death risk during typhoon Lekima and lag days.ResultsDuring the Lekima typhoon month, there were 3,366 deaths occurred in Jinan. Compared to unexposed periods, the acute death risk of non-accidental diseases (especially circulatory diseases), female and the older adults increased significantly in the second week after the typhoon. The maximum significant effect of circulatory disease deaths, female and older adult deaths were appeared on lag9, lag9, and lag13 respectively. And the typhoon-associated RR were 1.19 (95%CI:1.05,1.34), 1.28 (95%CI:1.08,1.52), and 1.22 (95%CI:1.06,1.42) respectively. The acute death risk of residents living in TQ and CQ increased significantly on Lag2 and Lag6 after the typhoon, respectively, while those living in LX, LC, HY, JY, and SH occurred from Lag 8 to Lag 13 after the typhoon. LC lasted the longest days.ConclusionsTyphoons would increase the vulnerability of residents living in Jinan which mainly occurred from the seventh day after the typhoon. Residents suffering from non-accidental diseases (circulatory diseases), female and the older adults were more vulnerable. The vulnerability of TQ and CQ occurred on Lag2 and Lag6 after typhoon Lekima, respectively, and the other areas except ZQ and PY occurred from Lag 8 to Lag 13. LC lasted the longest duration. Our findings emphasized the importance of the emergency response, which would help policymakers to identify vulnerable regions and populations accurately during typhoons and formulate the emergency response plan.

Performance of the European Society of Cardiology 0/1-hour algorithm with high-sensitivity cardiac troponin T at 90 days among patients with known coronary artery disease

BackgroundThe European Society of Cardiology (ESC) 0/1-h high sensitivity troponin T (hs-cTnT) algorithm does not differentiate risk based on known coronary artery disease (CAD: prior myocardial infarction [MI], coronary revascularization, or ≥ 70% coronary stenosis). We recently evaluated its performance among patients with known CAD at 30-days, but little is known about its longer-term risk prediction. The objective of this study is to determine and compare the performance of the algorithm at 90-days among patients with and without known CAD. MethodsWe performed a pre-planned subgroup analysis of the STOP-CP cohort, which prospectively enrolled ED patients ≥21 years old with symptoms suggestive of ACS without ST-elevation on initial ECG across 8 US sites (1/25/2017–9/6/2018). Participants with 0- and 1-h hs-cTnT measures (Roche, Basel, Switzerland) were stratified into rule-out, observe, and rule-in groups using the ESC 0/1-h algorithm. Algorithm performance was tested among patients with or without known CAD, as determined by the treating provider. The primary outcome was cardiac death or MI at 90-days. Fisher's exact tests were used to compare 90-day event and rule-out rates between patients with and without known CAD. Negative predictive values (NPVs) for 90-day cardiac death or MI with exact 95% confidence intervals were calculated and compared using Fisher's exact test. ResultsThe STOP-CP study accrued 1430 patients, of which 31.4% (449/1430) had known CAD. Cardiac death or MI at 90 days was more common in patients with known CAD than in those without [21.2% (95/449) vs. 10.0% (98/981); p < 0.001]. Using the ESC 0/1-h algorithm, 39.6% (178/449) of patients with known CAD and 66.1% (648/981) of patients without known CAD were ruled-out (p < 0.001). Among rule-out patients, 90-day cardiac death or MI occurred in 3.4% (6/178) of patients with known CAD and 1.2% (8/648) without known CAD (p = 0.09). NPV for 90-day cardiac death or MI was 96.6% (95%CI 92.8–98.8) among patients with known CAD and 98.8% (95%CI 97.6–99.5) in patients without known CAD (p = 0.09). ConclusionPatients with known CAD who were ruled-out using the ESC 0/1-h hs-cTnT algorithm had a high rate of missed 90-day cardiac events, suggesting that the ESC 0/1-h hs-cTnT algorithm may not be safe for use among patients with known CAD. Trial registrationHigh-Sensitivity Cardiac Troponin T to Optimize Chest Pain Risk Stratification (STOP-CP; ClinicalTrials.gov: NCT02984436; https://clinicaltrials.gov/ct2/show/NCT02984436).

The Use of ICD-9-CM Coding to Identify COVID-19 Diagnoses and Determine Risk Factors for 30-Day Death Rate in Hospitalized Patients in Italy: Retrospective Study.

In Italy, it has been difficult to accurately quantify hospital admissions of patients with a COVID-19 diagnosis using the Hospital Information System (HIS), mainly due to the heterogeneity of codes used in the hospital discharge records during different waves of the COVID-19 pandemic. The objective of this study was to define a specific combination of codes to identify the COVID-19 hospitalizations within the HIS and to investigate the risk factors associated with mortality due to COVID-19 among patients admitted to Italian hospitals in 2020. A retrospective study was conducted using the hospital discharge records, provided by more than 1300 public and private Italian hospitals. Inpatient hospitalizations were detected by implementing an algorithm based on specific International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code combinations. Hospitalizations were analyzed by different clinical presentations associated with COVID-19 diagnoses. In addition, 2 multivariable Cox regression models were performed among patients hospitalized "due to COVID-19" from January 1 to December 31, 2020, to investigate potential risk factors associated with 30-day death and the temporal changes over the course of the pandemic; in particular, the 30-day death rates during the first and the second waves were analyzed across 3 main geographical areas (North, Center, and South and Islands) and by discharge wards (ordinary and intensive care). We identified a total of 325,810 hospitalizations with COVID-19-related diagnosis codes. Among these, 73.4% (n=239,114) were classified as "due to COVID-19," 14.5% (n=47,416) as "SARS-CoV-2 positive, but not due to COVID-19," and 12.1% (n=39,280) as "suspected COVID-19" hospitalizations. The cohort of patients hospitalized "due to COVID-19" included 205,048 patients, with a median age of 72 years and a higher prevalence of male patients (n=124,181, 60.6%). The overall 30-day death rate among hospitalized patients due to COVID-19 was 9.9 per 1000 person-days. Mortality was lower for women (hazard ratio [HR]=0.83; P<.001) and for patients coming from high migration pressure countries, especially Northern Africans (HR=0.65; P<.001) and Central and Eastern Europeans (HR=0.66; P<.001), compared to patients coming from Italy and high-income countries. In the southern regions and the Islands, mortality was higher compared to the northern regions (HR=1.17; P<.001), especially during the second wave of COVID-19 among patients with a transfer to intensive care units (HR=2.52; P<.001). To our knowledge, the algorithm is the first attempt to define, at a national level, selection criteria for identifying COVID-19 hospitalizations within the HIS. The implemented algorithm will be used to monitor the pandemic over time, and the patients selected in 2020 will be followed up in the next years to assess the long-term effects of COVID-19.

Prevalence and prognostic implications of myocardial injury across different waves of COVID-19.

The prognostic ability of myocardial injury across different waves of the COVID-19 pandemic is not well established. The purpose of this study was to evaluate the prevalence and prognostic implications of myocardial injury in the first and sixth wave of COVID-19. We conducted a retrospective observational study that included patients admitted to the emergency department with COVID-19 with data on concentrations of cardiac troponin during the first and sixth wave. We compared the prevalence of myocardial injury and its predictive capacity for 30-day all-cause death in both waves. A total of 346 patients were included (1st wave 199 and 6th wave 147 patients). The prevalence of myocardial injury was 21% with non-significant differences between waves. Myocardial injury was associated, in both waves, with a higher prevalence of comorbidities and with an increased risk of 30-day all-cause death [1st wave HR: 3.73 (1.84-7.55); p < 0.001 and 6th wave HR: 3.13 (1.23-7.92); p = 0.016], with non-significant differences in predictive capacity between groups after ROC curve analysis [AUC: 1st wave 0.829 (95% CI: 0.764-0.895) and 6th wave 0.794 (95% CI: 0.711-0.876)]. As limitations, this is a retrospective study with a relatively small simple size and troponin assay was performed at the discretion of the emergency physician so selection bias could be present. In conclusion, the prevalence of myocardial injury and its prognostic capacity was similar in both waves despite vaccination programs. Myocardial injury predicts short-term mortality in all COVID-19 patients, so they should be treated intensively.

Safety and Effectiveness of the High-Sensitivity Cardiac Troponin HEART Pathway in Patients With Possible Acute Coronary Syndrome.

The HEART Pathway (History, Electrocardiogram, Age, Risk factors, Troponin) can be used with high-sensitivity cardiac troponin to risk stratify emergency department patients with possible acute coronary syndrome. However, data on whether a high-sensitivity HEART Pathway (hs-HP) are safe and effective is lacking. An interrupted time series study was conducted at 5 North Carolina sites in 26 126 adult emergency department patients being investigated for possible acute coronary syndrome and without ST-segment-elevation myocardial infarction. Patients were accrued into 16-month preimplementation and postimplementation cohorts with a 6-month wash-in phase. Preimplementation (January 2019 to April 2020), the traditional HEART Pathway was used with 0- and 3-hour contemporary troponin measures (Siemens). In the postimplementation period (November 2020 to February 2022), a modified hs-HP was used with 0- and 2-hour high-sensitivity cardiac troponin (Beckman Coulter) measures. The primary safety and effectiveness outcomes were 30-day all-cause death or myocardial infarction and 30-day hospitalizations. These outcomes and early discharge rate (emergency department discharge without stress testing or coronary angiography) were determined from health records and death index data. Outcomes were compared preimplementation versus postimplementation using χ2 tests and multivariable logistic regression to adjust for potential confounders. Preimplementation and postimplementation cohorts included 12 317 and 13 809 patients, respectively, of them 52.7% (13 767/26 126) were female with a median age of 54 years (interquartile range, 42-66). Rates of 30-day death or MI were 6.8% (945/13 809) postimplementation and 7.7% (948/12 317) preimplementation (adjusted odds ratio, 1.00 [95% CI, 0.90-1.11]). hs-HP implementation was associated with 19.9% (95% CI, 18.7%-21.1%) higher early discharges (post versus pre: 63.6% versus 43.7%; adjusted odds ratio, 2.22 [95% CI, 2.10-2.35]). The hs-HP was also associated with 16.1% (95% CI, 14.9%-17.3%) lower 30-day hospitalizations (postimplementation versus preimplementation, 31.4% versus 47.5%; adjusted odds ratio, 0.51 [95% CI, 0.48-0.54]). Among early discharge patients, death or myocardial infarction occurred in 0.5% (41/8780) postimplementation versus 0.4% (22/5383) preimplementation (P=0.61). hs-HP implementation is associated with increased early discharges without increasing adverse events. These findings support the use of a modified hs-HP to improve chest pain care.

Predicting outcomes following lower extremity open revascularization using machine learning

Lower extremity open revascularization is a treatment option for peripheral artery disease that carries significant peri-operative risks; however, outcome prediction tools remain limited. Using machine learning (ML), we developed automated algorithms that predict 30-day outcomes following lower extremity open revascularization. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity open revascularization for chronic atherosclerotic disease between 2011 and 2021. Input features included 37 pre-operative demographic/clinical variables. The primary outcome was 30-day major adverse limb event (MALE; composite of untreated loss of patency, major reintervention, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using tenfold cross-validation, we trained 6 ML models. Overall, 24,309 patients were included. The primary outcome of 30-day MALE or death occurred in 2349 (9.3%) patients. Our best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve (95% CI) of 0.93 (0.92–0.94). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.08. Our ML algorithm has potential for important utility in guiding risk mitigation strategies for patients being considered for lower extremity open revascularization to improve outcomes.

Abstract TP174: Balloon Expandable Stents versus Self-Expanding Stents for Treatment of Intracranial Arterial Stenosis: A Systematic Review and Meta-Analysis

Background: There is limited data regarding the comparison of balloon-expanding stents (BES) and self-expanding stents (SES) for the treatment of intracranial arterial stenosis. Methods: We conducted a systematic review to identify studies that compared SES and BES in patients with symptomatic intracranial arterial stenosis. Data were extracted from relevant studies found through a search of PubMed, Scopus, and Web of Science until May 2023. Statistical pooling with random-effects meta-analysis was undertaken to compare the rates/severity of post-procedure stenosis, technical success, stroke and/or mortality, long-term events, and restenosis rates between BES and SES. Results: A total of 20 studies were included. The standardized mean difference (SMD) for post-procedure stenosis (%) was significantly lower (SMD: -0.52, 95% confidence interval [CI]: -0.79 to -0.24, p &lt; 0.001, 10 studies involving 1515 patients) with BES. The odds for technical success were non-significantly lower (odds ratio [OR]: 0.99, 95% CI 0.43-2.26, p = 0.983, 9 studies involving 1001 patients) with BES. The odds for post-procedure 30-day stroke and/or death were significantly lower (OR 0.68, 95% CI: 0.50-0.94, p = 0.019, 15 studies involving 2431 patients), and stroke and/or death beyond 30 days were non-significantly lower (OR 0.64, 95% CI: 0.30-1.37, p = 0.250, 10 studies involving 947 patients) with BES. The odds for restenosis rate were significantly lower (OR 0.50, 95% CI: 0.31-0.80, p = 0.004, 13 studies involving 1115 patients) with BES. Conclusions: Compared with SES, BES was associated with lower rates of post-procedure 30-day stroke and/or death presumably due to less severe post-procedure stenosis in the treatment of symptomatic intracranial arterial stenosis.

Effect of Insulin Dependence on Perioperative Risk in Patients With Diabetes Undergoing Total Shoulder Arthroplasty.

Approximately 15% of patients who undergo total shoulder arthroplasty (TSA) have diabetes mellitus, and this group is particularly at risk for perioperative complications. The objective of this study was to quantify the effects of insulin dependence on the risk of 30-day perioperative adverse events after TSA in patients with diabetes mellitus using a large national database. We retrospectively identified patients with diabetes mellitus who underwent TSA in the National Surgical Quality Improvement Program (NSQIP) database from 2011 to 2020. Patients were grouped as having insulin-dependent or non-insulin-dependent diabetes mellitus. The primary outcome was 30-day complication, and secondary outcome variables included 30-day readmission, reoperation, and death. Multivariable logistic regression analyses adjusted for baseline differences were performed. The cohort included 5888 patients with diabetes mellitus who underwent TSA, with 1705 patients in the insulin-dependent group and 4183 patients in the non-insulin-dependent group. The 30-day postoperative complication rate was higher in patients with insulin-dependent diabetes mellitus (8.7%) than in patients with non-insulin-dependent diabetes mellitus (5.6%). The 30-day hospital readmission rate was higher in patients with insulin-dependent diabetes mellitus (4.8%) than in patients with non-insulin-dependent diabetes mellitus (3.3%). These significant differences in complication and readmission rates persisted in the multivariable logistic regression analyses after adjusting for baseline differences between the two groups. Reoperation and death were not associated with insulin dependence. Patients with insulin-dependent diabetes mellitus have higher odds of episode-of-care complication and readmission compared with patients with non-insulin-dependent diabetes mellitus, even after adjusting for between-group baseline differences. [Orthopedics. 2024;47(5):295-300.].

Efficacy and safety of Chinese herbal medicine as adjunctive therapy in sepsis patients with bloodstream infection: a propensity-matched analysis.

To investigate the efficacy and safety of Chinese herbal medicine in treating sepsis patients with bloodstream infection. A 6-year retrospective study was carried out at a university hospital in China. Adult sepsis patients with bloodstream infection were included. The primary outcome was 28-day mortality after admission. Propensity score method was used to adjust for possible confounding. 28-day mortality was estimated by Kaplan-Meier analysis and compared using the log-rank test. Cox regression analysis was carried out to identify factors impacting in-hospital mortality outcomes. Following the application of the propensity score method, a total of 176 patients were included. The all-cause 28-day mortality in the control group and Chinese herbal medicine group was 21.6% and 14.8%, respectively. Kaplan-Meier survival analysis showed that Chinese herbal medicine was associated with a lower hazard ratio (HR) in all-cause 28-day death compared with the control group [HR = 0.44, 95% CI(0.22, 0.90), P < 0.05]. The complications were similar between the two groups (P >0.05). Blood-activating and stasis-eliminating herb administration was associated with reduced in-hospital mortality among sepsis patients with bloodstream infection [HR = 0.54, 95% CI(0.34, 0.94), P < 0.05]. Chinese herbal medicine, especially the blood-activating and stasis-eliminating herb, might have certain efficacy and safety in treating sepsis patients with bloodstream infection. Clinicians should prescribe blood-activating and stasis-eliminating herb in treating these two coalescent critical diseases as long as no contraindications exist. However, further studies are needed to validate our results.

Disparities and effectiveness of COVID-19 vaccine policies in three representative European countries

ObjectiveThe aim of this study was to examine the Coronavirus disease 2019(COVID-19) vaccine policies disparities and effectiveness in Germany, Denmark and Bulgaria, with a view to providing lessons for global vaccination and response to possible outbreak risks.MethodsThis study analyzed big data through public information on the official websites of the Ministries of Health of the European Union, Germany, Denmark and Bulgaria and the official websites of the World Health Organization. We systematically summarized the COVID-19 vaccine policies of the three countries, and selected the following six indicators for cross-cutting vaccination comparisons: COVID-19 vaccine doses administered per 100 people, COVID-19 vaccination rate, the share of people with fully vaccinated, the share of people only partly vaccinated, cumulative confirmed COVID-19 cases per million, cumulative confirmed COVID-19 deaths per million. Meanwhile, we selected the following four indicators for measuring the effectiveness of COVID-19 vaccine policy implementation: daily cases per million, daily deaths per million, the effective reproduction rate (Rt), the moving-average case fatality rate (CFR).ResultsAlthough these three EU countries had the same start time for vaccination, and the COVID-19 vaccine supply was coordinated by the EU, there are still differences in vaccination priorities, vaccination types, and vaccine appointment methods. Compared to Germany and Denmark, Bulgaria had the least efficient vaccination efforts and the worst vaccination coverage, with a vaccination rate of just over 30% as of June 2023, and the maximum daily deaths per million since vaccination began in the country was more than three times that of the other two countries. From the perspective of implementation effect, vaccination has a certain effect on reducing infection rate and death rate, but the spread of new mutant strains obviously aggravates the severity of the epidemic and reduces the effectiveness of the vaccine. Among them, the spread of the Omicron mutant strain had the most serious impact on the three countries, showing an obvious epidemic peak.ConclusionsExpanding vaccination coverage has played a positive role in reducing COVID-19 infection and mortality rates and stabilizing Rt. Priority vaccination strategies targeting older people and at-risk groups have been shown to be effective in reducing COVID-19 case severity and mortality in the population. However, the emergence and spread of new variant strains, and the relaxation of epidemic prevention policies, still led to multiple outbreaks peaking. In addition, vaccine hesitancy, mistrust in government and ill-prepared health systems are hampering vaccination efforts. Among the notable ones are divergent types of responses to vaccine safety issue could fuel mistrust and hesitancy around vaccination. At this stage, it is also necessary to continue to include COVID-19 vaccination in priority vaccination plans and promote booster vaccination to prevent severe illness and death. Improving the fairness of vaccine distribution and reducing the degree of vaccine hesitancy are the focus of future vaccination work.

Assessing Internet Search Models in Predicting Daily New COVID-19 Cases and Deaths in South Korea.

Search data were found to be useful variables for COVID-19 trend prediction. In this study, we aimed to investigate the performance of online search models in state space models (SSMs), linear regression (LR) models, and generalized linear models (GLMs) for South Korean data from January 20, 2020, to July 31, 2021. Principal component analysis (PCA) was run to construct the composite features which were later used in model development. Values of root mean squared error (RMSE), peak day error (PDE), and peak magnitude error (PME) were defined as loss functions. Results showed that integrating search data in the models for short- and long-term prediction resulted in a low level of RMSE values, particularly for SSMs. Findings indicated that type of model used highly impacts the performance of prediction and interpretability of the model. Furthermore, PDE and PME could be beneficial to be included in the evaluation of peaks.

Implementación de un modelo de pronóstico de series de tiempo para estiman exceso de muertes en Brasil en 2020

Goals: The aim of this paper is to understand the behavior of the Covid-19 pandemic on the national Brazilian scenario and describe how it affected the mortality rate. Methods: Implement a predictor model using ARIMA modeling concepts and data extracted from the Unified Health System database, in order to estimate the number of deaths caused by COVID-19 in Brazil during 2020. Results: COVID-19 is estimated to have contributed, on average, to a surplus of 713 daily deaths. Conclusion: Even considering the records of deaths by COVID-19 on the result of the prediction, it is observed that the combination is below the real curve, which indicates that there is underreporting of deaths caused by this disease during the year 2020 in Brazil.