30-day Death Research Articles

Spatiotemporal distribution and lag effect of extreme temperature exposure on mortality of residents in Jiangsu, China

BackgroundWith the ever-increasing occurrence of extreme weather events as a result of global climate change, the impact of extreme temperatures on human health has become a critical area of concern. Specifically, it is imperative to investigate the impact of extreme weather conditions on the health of residents. MethodsIn this study, we analyze the daily death data from 13 prefecture-level cities in Jiangsu Province from January 2014 to September 2022, using the distributed lag nonlinear model (DLNM) to comprehensively account for factors such as relative humidity, atmospheric pressure, air pollutants, and other factors to evaluate the lag and cumulative effects of extreme low temperature and high temperature on the death of residents across different age groups. Additionally, we utilize the Geographical Detector to analyze the effects of various meteorological and environmental factors on the distribution of resident death in Jiangsu Province. This provides valuable insights that can guide health authorities in decision-making and in the protection of residents. ResultsThe experimental results indicate that both extreme low and high temperatures increase the mortality of residents. We observe that the impact of extreme low temperatures has a delayed effect, peaking after 3–5 days and lasting up to 11–21 days. In contrast, the impact of extreme high temperature is greatest on the first day, and lasts only 2–4 days. ConclusionBoth extreme high and low temperatures increase the mortality of residents, with the former being more transient and stronger and the latter being more persistent and slower. Furthermore, residents over 75 years of age are more vulnerable to the effects of extreme temperatures. Finally, we note that the spatial distribution of resident deaths is most closely associated consistent with the spatial distribution of daily mean temperature, and there is significant spatial heterogeneity in deaths among residents in Jiangsu Province.

Predicting clinical outcomes of SARS-CoV-2 infection during the Omicron wave using machine learning.

The Omicron SARS-CoV-2 variant continues to strain healthcare systems. Developing tools that facilitate the identification of patients at highest risk of adverse outcomes is a priority. The study objectives are to develop population-scale predictive models that: 1) identify predictors of adverse outcomes with Omicron surge SARS-CoV-2 infections, and 2) predict the impact of prioritized vaccination of high-risk groups for said outcome. We prepared a retrospective longitudinal observational study of a national cohort of 172,814 patients in the U.S. Veteran Health Administration who tested positive for SARS-CoV-2 from January 15 to August 15, 2022. We utilized sociodemographic characteristics, comorbidities, and vaccination status, at time of testing positive for SARS-CoV-2 to predict hospitalization, escalation of care (high-flow oxygen, mechanical ventilation, vasopressor use, dialysis, or extracorporeal membrane oxygenation), and death within 30 days. Machine learning models demonstrated that advanced age, high comorbidity burden, lower body mass index, unvaccinated status, and oral anticoagulant use were the important predictors of hospitalization and escalation of care. Similar factors predicted death. However, anticoagulant use did not predict mortality risk. The all-cause death model showed the highest discrimination (Area Under the Curve (AUC) = 0.903, 95% Confidence Interval (CI): 0.895, 0.911) followed by hospitalization (AUC = 0.822, CI: 0.818, 0.826), then escalation of care (AUC = 0.793, CI: 0.784, 0.805). Assuming a vaccine efficacy range of 70.8 to 78.7%, our simulations projected that targeted prevention in the highest risk group may have reduced 30-day hospitalization and death in more than 2 of 5 unvaccinated patients.

Cardiovascular Outcomes Among Patients with Acute Coronary Syndromes and Diabetes: Results from ACS QUIK Trial in India.

Despite cardiovascular disease being the leading cause of death in India, limited data exist regarding the factors associated with outcomes in patients with diabetes who suffer acute myocardial infarction (AMI). We examined 21,374 patients with AMI enrolled in the ACS QUIK trial. We compared in-hospital and 30-day major adverse cardiac events including death, re-infarction, stroke, or major bleeding in those with and without diabetes. The associations between diabetes and cardiac outcomes were adjusted for presentation and in-hospital management using logistic regression. Mean ± SD age was 60.1 ± 12.0 years, 24.3% were females, and 44.4% had diabetes. Those with diabetes were more likely to be older, female, hypertensive, and have higher Killip class but less likely to present with STEMI. Patients with diabetes had longer symptoms onset-to-arrival (median 225 vs 290 min; P < 0.001) and, in case of STEMI, longer door-to-balloon times (median, 75 vs 91 min; P < 0.001). Diabetes was independently associated with higher in-hospital death (adjusted odds ratio [aOR], 1.46; 95% CI, 1.12-1.89), in-hospital reinfarction (aOR, 1.52; 95% CI, 1.15-2.02), 30-day MACE (aOR, 1.33; 95% CI, 1.14-1.55) and 30-day death (aOR, 1.40; 95%CI, 1.16-1.69) but not 30-day stroke or 30-day major bleeding. Among patients presenting with AMI in Kerala, India, a considerable proportion has diabetes and are at increased risk for in-hospital and 30-day adverse cardiovascular outcomes. Increased awareness of the increased cardiovascular risk and attention to the implementation of established cardiovascular therapies are indicated for patients with diabetes in lower-middle-income countries who develop AMI. ClinicalTrials.gov Unique identifier: NCT02256658.

High-risk subgroups were not identified to benefit from thromboprophylaxis after hospitalization for COVID-19

BackgroundThe Accelerating COVID-19 Therapeutic Interventions and Vaccines-4c (ACTIV-4c) trial investigated prophylactic apixaban for 30 days following hospitalization for COVID-19. The overall incidence of early postdischarge death or thromboembolism was low, and the trial was closed early. ObjectivesTo identify a high-risk patient population who might benefit from postdischarge thromboprophylaxis through subgroup analyses stratified by age, race/ethnicity, obesity, D-dimer elevation, World Health Organization score, and modified International Medical Prevention Registry on Venous Thromboembolism score on 30-day composite outcome of all-cause death, arterial thromboembolism (ATE), and venous thromboembolism (VTE). MethodsCumulative incidences of all-cause death, ATE, and VTE within 30 days were described for each subgroup. Time to death, ATE, or VTE by 30 days was analyzed using Cox proportional hazard models with interaction testing for each subgroup. ResultsAmong 1217 patients randomized to apixaban or placebo group, 32% were >60 years old. Modified International Medical Prevention Registry on Venous Thromboembolism score was ≥4 in 2% and 2 or 3 with an elevated D-dimer in an additional 9% of participants. The overall incidence of the primary endpoint was 2.13% in the apixaban group and 2.31% in the placebo group. At day 30, similar rates of the primary endpoint occurred within subgroups, except for participants aged >60 years. No benefit of thromboprophylaxis was seen in any subgroup. ConclusionThe combined incidence of 30-day death, ATE, and VTE was low in patients who survived COVID-19 hospitalization, except in patients over age 60 years. Due to the limited number of events, the findings remain inconclusive; nonetheless, the study did not identify a high-risk subgroup that would derive benefits from extended thromboprophylaxis.

Assessment on Inpatient Glycaemic Control in General Medical Wards, Putrajaya Hospital.

Inpatient hyperglycaemia is common and associated with poor outcomes such as increased mortality and prolonged hospital stay. This study aimed to determine the prevalence of inpatient hyperglycaemia and glycaemic control in Putrajaya Hospital, Malaysia. Secondary objectives were to compare the length of stay (LOS), 30-day readmission rate, and death between controlled and uncontrolled glycaemic groups. This cross-sectional study was conducted between 1 July and 31 December 2019 among patients in medical wards who had a blood glucose (BG) level of > 7.8 mmol/L and stayed in the wards for ≥ 24 h. We retrieved information on demographics, diabetes history and BG profiles. The definition of controlled glycaemic status is when ≥ 80% of BG readings were between 4.0 mmol/L and 10.0 mmol/L during the hospital stay. The prevalence of inpatient hyperglycaemia was 55.2%. There were 841 patients who met the eligibility criteria; their mean age was 60 (13.8) years old. Most (79.4%) of the patients were Malay and 53.9% were male. There were 452 (53.7%) patients in the uncontrolled group. They were younger and admitted with more kidney complications compared to those in the controlled group. The median LOS for both groups was 3 (2) days. The uncontrolled group showed a higher percentage of readmission within 30 days (7.5% versus 4.6 %) and death during admission (3.3% versus 1.6 %) (P = 0.100 and P = 0.082). The prevalence of inpatient hyperglycaemia was high. More than half of them had uncontrolled BG. Both groups had a similar average length of stay. The 30-day readmission rate and death during admission were higher in the uncontrolled group, although statistically not significant.

C-reactive Protein and Risk of Right Ventricular Dysfunction and Mortality in Patients With Acute Symptomatic Pulmonary Embolism

BackgroundRight ventricle (RV) dysfunction increases the risk of death from pulmonary embolism (PE). C-reactive protein (CRP) might identify RV inflammation and dysfunction in patients with PE. MethodsThis cohort study enrolled consecutive stable patients with acute PE between 2017 and 2023. We stratified patients by quartiles of CRP. We evaluated the association between CRP quartiles and the presence of RV dysfunction, and used multivariable models to assess for an association between CRP and the outcomes of all-cause and PE-specific mortality during the 30 days of follow-up after PE diagnosis. ResultsThe study included 633 stable patients with PE. Patients without RV dysfunction had significantly lower median (IQR) CRP levels compared with patients with RV dysfunction (n=509, 31.7 [10.0–76.4]mg/L vs n=124, 45.4 [16.0–111.4]mg/L; P=0.018). CRP showed a statistically significant positive association with the presence of RV dysfunction (P<0.01). On multivariable analysis, CRP level was not significantly associated with 30-day all-cause mortality (adjusted odds ratio [OR] per mg/L increment, 1.00; 95% CI, 1.00–1.01; P=0.095), but higher CRP was associated with significantly higher PE-related mortality (adjusted OR, 1.01; 95% CI, 1.00–1.01; P=0.026). Compared with patients in CRP quartile 1, patients in quartiles 2, 3, and 4 had a stepwise increase in the adjusted odds of 30-day all-cause death of 2.41 (P=0.148), 3.04 (P=0.062), and 3.15 (P=0.052), respectively. ConclusionsAs an indicator of RV dysfunction, CRP may improve risk stratification algorithms for hemodynamically stable patients with acute symptomatic PE.

Determining if the prognostic nutritional index can predict outcomes in community acquired bacterial pneumonia

BackgroundThe Prognostic Nutritional Index (PNI) uses albumin levels and total lymphocyte count to predict the relationship between immune-nutritional state and prognosis in a variety of diseases, however it has not been studied in community acquired bacterial pneumonia (CABP). We conducted a historical cohort study to determine if there was an association between PNI and clinical outcomes in patients with CABP. MethodsWe reviewed 204 adult patients with confirmed CABP, and calculated admission PNI and Neutrophil-to-Lymphocyte Ratio (NLR). A comparative analysis was performed to determine the association of these values, as well as other risk factors, with the primary outcomes of 30-day readmissions and death. ResultsOf the 204 patients, 56.9% (116) were male, 48% (98) were black/African American and the mean age was 63.2 ± 16.1 years. The NLR was neither associated with death nor 30-day readmission. The mean PNI in those who survived was 34.7 ± 4.5, compared to 30.1 ± 6.5, in those who died, p < 0.001. From multivariable analysis after controlling for the Charlson score and age, every one-unit increase in the PNI decreased the risk of death by 13.6%. The PNI was not associated with readmissions. ConclusionsThese findings suggest that poor immune and nutritional states, as reflected by PNI, both contribute to mortality, with a significant negative correlation between PNI and death in CABP. PNI was predictive of mortality in this patient cohort; NLR was not. Monitoring of albumin and lymphocyte count in CABP can provide a means for prevention and early intervention.

Sedation versus general anesthesia on all-cause mortality in patients undergoing percutaneous procedures: a systematic review and meta-analysis

BackgroundThe comparison between sedation and general anesthesia (GA) in terms of all-cause mortality remains a subject of ongoing debate. The primary objective of our study was to investigate the impact of GA and sedation on all-cause mortality in order to provide clarity on this controversial topic.MethodsA systematic review and meta-analysis were conducted, incorporating cohort studies and RCTs about postoperative all-cause mortality. Comprehensive searches were performed in the PubMed, EMBASE, and Cochrane Library databases, with the search period extending until February 28, 2023. Two independent reviewers extracted the relevant information, including the number of deaths, survivals, and risk effect values at various time points following surgery, and these data were subsequently pooled and analyzed using a random effects model.ResultsA total of 58 studies were included in the analysis, with a majority focusing on endovascular surgery. The findings of our analysis indicated that, overall, and in most subgroup analyses, sedation exhibited superiority over GA in terms of in-hospital and 30-day mortality. However, no significant difference was observed in subgroup analyses specific to cerebrovascular surgery. About 90-day mortality, the majority of studies centered around cerebrovascular surgery. Although the overall pooled results showed a difference between sedation and GA, no distinction was observed between the pooled ORs and the subgroup analyses based on RCTs and matched cohort studies. For one-year all-cause mortality, all included studies focused on cardiac and macrovascular surgery. No difference was found between the HRs and the results derived from RCTs and matched cohort studies.ConclusionsThe results suggested a potential superiority of sedation over GA, particularly in the context of cardiac and macrovascular surgery, mitigating the risk of in-hospital and 30-day death. However, for the longer postoperative periods, this difference remains uncertain.Trial registrationPROSPERO CRD42023399151; registered 24 February 2023.

INAR(1) process with weighted negative binomial Lindley distributed innovations and applications to criminal and COVID-19 data

In this study, we introduce a pliant stationary first-order integer-valued autoregressive (INAR) process with weighted negative binomial Lindley innovations. The main properties of the model are derived. The methods of conditional maximum likelihood, conditional least square and Yule-Walker are used for estimating the process parameters, while the efficiency of these three methods is evaluated through a simulation study. Finally, the practical aspect of the proposed INAR(1) process is discussed on two time series of the monthly number of criminal mischief reports in Pittsburgh and the daily COVID-19 death cases in Paraguay.

Construction of a predictive model of death for sepsis-associated acute kidney injury

To establish a predictive model nomogram for 30-day death in patients with sepsis-associated acute kidney injury (SA-AKI) by using the data from the large international database, the Electronic Intensive Care Unit-Collaborative Research Database (eICU-CRD), and to validate its predictive performance. A retrospective cohort study was conducted using data from the eICU-CRD. Data of SA-AKI patients were screened from the eICU-CRD database, including demographic characteristics, medical history, SA-AKI type, Kidney Disease: Improving Global Outcomes (KDIGO)-AKI staging, severity of illness scores, vital signs, laboratory indicators, and treatment measures; with admission time as the observation start point, death as the outcome event, and a follow-up time of 30 days. Relevant variables of patients with different 30-day prognoses were compared. Univariate Logistic regression analysis and multivariate Logistic regression forward likelihood ratio analysis were used to screen for risk factors associated with 30-day death in SA-AKI patients, and a predictive model nomogram was constructed. Receiver operator characteristic curve (ROC curve), calibration curve, and Hosmer-Lemeshow test were used to validate the predictive performance of the model. A total of 201 SA-AKI patients' data were finally enrolled, among which 51 survived for 30 days and 150 died, with a mortality of 74.63%. Compared with the survival group, patients in the death group were older [years old: 68 (60, 78) vs. 59 (52, 69), P < 0.01], had lower body weight, proportion of transient SA-AKI, platelet count (PLT) and blood glucose [body weight (kg): 79 (65, 95) vs. 91 (71, 127), proportion of transient SA-AKI: 61.33% (92/150) vs. 82.35% (42/51), PLT (×109/L): 207 (116, 313) vs. 260 (176, 338), blood glucose (mmol/L): 5.5 (4.4, 7.1) vs. 6.4 (5.1, 7.6), all P < 0.05] and higher proportion of persistent SA-AKI, sequential organ failure assessment (SOFA) score, lactic acid (Lac), and total bilirubin [TBil; proportion of persistent SA-AKI: 38.67% (58/150) vs. 17.65% (9/51), SOFA score: 7 (5, 22) vs. 5 (2, 7), Lac (mmol/L): 0.4 (0.2, 0.7) vs. 0.3 (0.2, 0.4), TBil (μmol/L): 41.0 (17.1, 51.3) vs. 18.8 (17.1, 34.2), all P < 0.05]. Univariate Logistic regression analysis showed that age [odds ratio (OR) = 1.035, 95% confidence interval (95%CI) was 1.013-1.058, P = 0.002], body weight (OR = 0.987, 95%CI was 0.977-0.996, P = 0.007), persistent SA-AKI (OR = 2.942, 95%CI was 1.333-6.491, P = 0.008), SOFA score (OR = 1.073, 95%CI was 1.020-1.129, P = 0.006), PLT (OR = 0.998, 95%CI was 0.996-1.000, P = 0.034), Lac (OR = 1.142, 95%CI was 1.009-1.292, P = 0.035), TBil (OR = 1.422, 95%CI was 1.070-1.890, P = 0.015) were associated with 30-day death risk in SA-AKI patients. Multivariate Logistic regression forward likelihood ratio analysis showed that age (OR = 1.051, 95%CI was 1.023-1.079, P = 0.000), body weight (OR = 0.985, 95%CI was 0.974-0.995, P = 0.005), cardiovascular disease (OR = 9.055, 95%CI was 1.037-79.084, P = 0.046), persistent SA-AKI (OR = 3.020, 95%CI was 1.258-7.249, P = 0.013), SOFA score (OR = 1.076, 95%CI was 1.013-1.143, P = 0.017), and PLT (OR = 0.997, 95%CI was 0.995-1.000, P = 0.030) were independent risk factors for 30-day death in SA-AKI patients. Based on the above risk factors, a predictive model nomogram for 30-day death in SA-AKI patients was constructed. ROC curve analysis showed that the area under the ROC curve (AUC) of the model was 0.798 (95%CI was 0.722-0.873), with a sensitivity of 86.7% and a specificity of 62.7%. Calibration curve showed that the fitted curve was close to the standard line, indicating that the predicted probability was close to the actual probability, suggesting good predictive performance of the model. Hosmer-Lemeshow test showed χ 2 = 6.393, df = 8, P = 0.603 > 0.05, suggesting that the model could fit the observed data well. The quality of model fitting was judged by the accuracy of model prediction. The results showed that the prediction accuracy rate of the model was 95.3%, and the overall prediction accuracy rate of the model was 81.6%, indicating good model fitting. A predictive model for 30-day death in SA-AKI patients based on risk factors can be successfully constructed, and the model has high accuracy, sensitivity, reliability, and certain specificity, which can help to early identify high-risk patients for death and adopt more proactive treatment strategies.

Constructing a predictive model for the death risk of patients with septic shock based on supervised machine learning algorithms

To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms. The patients with septic shock meeting the Sepsis-3 criteria were selected from Medical Information Mart for Intensive Care-IV v2.0 (MIMIC-IV v2.0). According to the principle of random allocation, 70% of these patients were used as the training set, and 30% as the validation set. Relevant predictive variables were extracted from three aspects: demographic characteristics and basic vital signs, serum indicators within 24 hours of intensive care unit (ICU) admission and complications possibly affecting indicators, functional scoring and advanced life support. The predictive efficacy of models constructed using five mainstream machine learning algorithms including decision tree classification and regression tree (CART), random forest (RF), support vector machine (SVM), linear regression (LR), and super learner [SL; combined CART, RF and extreme gradient boosting (XGBoost)] for 28-day death in patients with septic shock was compared, and the best algorithm model was selected. The optimal predictive variables were determined by intersecting the results from LASSO regression, RF, and XGBoost algorithms, and a predictive model was constructed. The predictive efficacy of the model was validated by drawing receiver operator characteristic curve (ROC curve), the accuracy of the model was assessed using calibration curves, and the practicality of the model was verified through decision curve analysis (DCA). A total of 3 295 patients with septic shock were included, with 2 164 surviving and 1 131 dying within 28 days, resulting in a mortality of 34.32%. Of these, 2 307 were in the training set (with 792 deaths within 28 days, a mortality of 34.33%), and 988 in the validation set (with 339 deaths within 28 days, a mortality of 34.31%). Five machine learning models were established based on the training set data. After including variables at three aspects, the area under the ROC curve (AUC) of RF, SVM, and LR machine learning algorithm models for predicting 28-day death in septic shock patients in the validation set was 0.823 [95% confidence interval (95%CI) was 0.795-0.849], 0.823 (95%CI was 0.796-0.849), and 0.810 (95%CI was 0.782-0.838), respectively, which were higher than that of the CART algorithm model (AUC = 0.750, 95%CI was 0.717-0.782) and SL algorithm model (AUC = 0.756, 95%CI was 0.724-0.789). Thus above three algorithm models were determined to be the best algorithm models. After integrating variables from three aspects, 16 optimal predictive variables were identified through intersection by LASSO regression, RF, and XGBoost algorithms, including the highest pH value, the highest albumin (Alb), the highest body temperature, the lowest lactic acid (Lac), the highest Lac, the highest serum creatinine (SCr), the highest Ca2+, the lowest hemoglobin (Hb), the lowest white blood cell count (WBC), age, simplified acute physiology score III (SAPS III), the highest WBC, acute physiology score III (APS III), the lowest Na+, body mass index (BMI), and the shortest activated partial thromboplastin time (APTT) within 24 hours of ICU admission. ROC curve analysis showed that the Logistic regression model constructed with above 16 optimal predictive variables was the best predictive model, with an AUC of 0.806 (95%CI was 0.778-0.835) in the validation set. The calibration curve and DCA curve showed that this model had high accuracy and the highest net benefit could reach 0.3, which was significantly outperforming traditional models based on single functional score [APS III score, SAPS III score, and sequential organ failure assessment (SOFA) score] with AUC (95%CI) of 0.746 (0.715-0.778), 0.765 (0.734-0.796), and 0.625 (0.589-0.661), respectively. The Logistic regression model, constructed using 16 optimal predictive variables including pH value, Alb, body temperature, Lac, SCr, Ca2+, Hb, WBC, SAPS III score, APS III score, Na+, BMI, and APTT, is identified as the best predictive model for the 28-day death risk in patients with septic shock. Its performance is stable, with high discriminative ability and accuracy.

Cardiovascular outcomes in solid organ transplant recipients undergoing cardiac surgery: A matched pair analysis.

This study aimed to compare postoperative outcomes after cardiac surgery in solid-organ transplant recipients and nontransplant patients. We performed a retrospective analysis of 78 consecutive transplant recipients who underwent cardiac surgery at Asan Medical Center between 2000 and 2022 and were matched with 312 nontransplant patients who underwent cardiac surgery at a 1:4 ratio. The outcomes included 30-day mortality, all-cause death, cardiac death, readmission, and cardiac readmission. There was no significant difference in baseline characteristics between the two groups. The most common type of cardiac surgery performed in solid organ transplant recipients was isolated valve surgery, followed by isolated CABG. The 30-day mortality was not significantly different between transplant recipients and nontransplant patients (3.9%vs. 3.5%; P>.99). Solid organ transplant recipients showed a higher all-cause mortality compared to nontransplant patients (29.1%vs. 14.3% at 5 years; P=.001); however, there was no significant difference in cardiac death between the two groups (2.6%vs. 3.2% at 5 years; P=.80). In addition, the readmission and cardiac readmission rates showed comparable findings to that of mortality. Cardiac surgery can be performed safely in solid organ transplant recipients, with postoperative cardiovascular outcomes comparable to those observed in nontransplant patients.

Relationship between 24 h blood pressure variability and mortality in acute myocardial infarction patients.

In recent years, the mortality of patients with AMI has not declined significantly. The relationship between blood pressure variability (BPV) and acute myocardial infarction (AMI) is unclear. We explored the relationship between 24-h BPV and mortality in patients with AMI. The mortality of patients with AMI is related to BPV. We hope to provide therapeutic ideas for reducing the risk of death in patients with AMI. This is a retrospective cohort study. We extracted and analyzed data from the MIMIC-IV 2.0, which was established in 1999 under the auspices of the National Institutes of Health (America). The average real variability (ARV) was calculated for the first 24-h blood pressure measurement after patients with AMI were admitted to the intensive care unit (ICU). Patients were divided into four groups according to ARV quartiles. The outcomes were 30-day, 1-year, and 3-year all-cause mortalities. Data were analyzed using Cox regression, Kaplan-Meier curves, and restricted cubic spline (RCS) curves. We enrolled 1291 patients with AMI, including 475 female. The patients were divided into four groups according to the qualities of diastolic blood pressure (DBP)-ARV. There were significant differences in the 30-day, 1-year and 3-year mortality among the four groups (p = .02, p < .001, p < .001, respectively). After adjustment for confounding factors, systolic blood pressure (SBP)-ARV could not predict AMI patient mortality (p > .05), while the highest DBP-ARV was associated strongly with increased 30-day mortality (HR: 2.291, 95% CI 1.260-4.168), 1-year mortality (HR: 1.933, 95% CI 1.316-2.840) and 3-year mortality (HR: 1.743, 95% CI 1.235-2.461). Kaplan-Meier curves demonstrated that, regardless of SBP or DBP, the long-term survival probabilities of patients in the highest ARV group were significantly lower than that of those in other groups. RCS curves showed that the death risk of patients with AMI first decreased and then increased with the increase in ARV when DBP-ARV < 8.04. The 30-day death risk first increased and then decreased, and the 1-year and 3-year death risks increased and then stabilized with ARV increase when DBP-ARV > 8.04. This study showed that patients with AMI may have an increased risk of short- and long-term death if their DBP-ARV is higher or lower during the first 24-h in ICU.

Hybrid total arterial minimally invasive off-pump coronary revascularization and percutaneous coronary intervention strategy for multivessel coronary artery disease: a cohort study with a median 11-year follow-up.

Hybrid coronary revascularization (HCR) is a treatment approach that combines the benefits of coronary artery bypass grafting (CABG) techniques such as minimally invasive direct coronary artery bypass (MIDCAB) or minimally invasive multivessel CABG (MICS-CABG) with percutaneous coronary intervention (PCI) for carefully selected patients with multivessel coronary artery disease (MV CAD). The extant body of research primarily concentrates on the comparison of outcomes between HCR and CABG or PCI. Furthermore, HCR is defined primarily as MIDCAB and PCI. Given the various criteria for HCR identified in the current body of literature, as well as several hybrid revascularization techniques, our primary goal was to analyse the characteristics and track the development of HCR patients operated on in our centre (Robert Bosch Hospital) over both short and long periods of time. Additionally, we sought to validate the practical challenges that arise during the implementation of an HCR methodology. This cohort study included 138 patients with MV CAD who had an HCR approach in conjunction with isolated total arterial off-pump MICS-CABG or MIDCAB between 2007 and 2018 at Robert Bosch Hospital in Stuttgart. Data on major adverse cardiac and cerebral events (MACCE), defined as all-cause mortality, myocardial infarction, repeat revascularization and stroke were gathered through a questionnaire. Long-term follow-up, with a mean duration of 8.7±0.3 years and a median duration of 11 years, was available for a significant majority of the patients (92.8%, n=128). The average age was 69.6±11.2 years, with 79% being male. The mean European System for Cardiac Operative Risk Evaluation score I additive (EuroSCORE I) additive was 7.6±10.2 and the mean SYNergy between PCI with TAXUS and Cardiac Surgery (SYNTAX) Score I was 22.9±9.4. A total of 97 MIDCAB surgeries and 41 MICS-CABG procedures were performed without any instances of conversion to sternotomy or cardiopulmonary bypass (CPB). A total of 70 patients, or 50.7% of the sample, received the planned PCI treatment. This percentage was substantially lower in the subgroup with chronic CAD, with just 27, equivalent to 39.1%. The observed 30-day death rate was 2.1% (3/138). During follow-up, 3 myocardial infarctions, 18 PCI repeats, no CABG, and 4 strokes occurred. From 128 followed-up patients, 28 died (21.9%), 7 of which were heart deaths (5.5%). Total MACCE was 36.7%. The survival rates at 3 and 5 years were 92% and 85% respectively. Patients who didn't get the planned PCI had a mean survival rate of 6.8-9.1 years, while those with completed hybrid treatment had a higher mean survival rate of 8.4-10.2 years. In selected individuals with MVCAD, current evidence suggests that HCR is a safe and effective coronary artery revascularization approach. After coronary bypass surgery, the attention going forward needs to be devoted toward the organization of the PCI step in the treatment process.

IJCM_290A: Death Audit Analysis from a single Covid-19 DCH of Madhya Pradesh, India

Background: In response to the escalating Covid-19-related fatalities in smaller towns and villages, the State Government initiated a daily death audit through the Death Audit Committee in each district. The aim of committee was to comprehensively review Covid-19-related deaths, utilizing the findings to inform policy decisions and prevent future mortalities. Objectives: To gain critical insights into the circumstances surrounding Covid-19 deaths, aiding in the identification of gaps and the development of strategies to avert future fatalities. Methodology: It was a cross-sectional study conducted in DCH Datia, the research encompassed all Covid-19-related deaths in Datia and Niwari districts during the first and second waves of the pandemic. The study period extended from March 2020 to July 2021, encompassing both waves. A total of 118 death audits were conducted by the Govt. Medical College Datia team, covering aspects such as age, gender, duration of ICU admission, comorbidities, and treatment including Remdesivir administration. Results: The second wave exhibited a significantly higher death rate (11.7%) compared to the first wave (0.04%). Among the 118 death audits, males accounted for 70 deaths (59.32%), with a majority (24.28%) occurring in the 51-60 age group. The presence of comorbidities (42.37%) was linked to increased mortality. Longer stay was associated with increased mortality. Remdesivir was administered in 39.83% of cases. Conclusion: The study underscores the heightened risk associated with long duration of stay and the impact of comorbidities on mortality. Further research on the efficacy of Remdesivir is warranted. The findings may help in shaping the policy &amp; strategy in future outbreaks both at district &amp; state level

Validation of the ACC Expert Consensus Decision Pathway for Patients With Chest Pain

BackgroundThe American College of Cardiology (ACC) recently published an Expert Consensus Decision Pathway for chest pain. ObjectivesThe purpose of this study was to validate the ACC Pathway in a multisite U.S. cohort. MethodsAn observational cohort study of adults with possible acute coronary syndrome was conducted. Patients were accrued from 5 U.S. Emergency Departments (November 1, 2020, to July 31, 2022). ECGs and 0- and 2-hour high-sensitivity troponin (Beckman Coulter) measures were used to stratify patients according to the ACC Pathway. The primary safety outcome was 30-day all-cause death or myocardial infarction (MI). Efficacy was defined as the proportion stratified to the rule-out zone. Negative predictive value for 30-day death or MI was assessed among the whole cohort and in a subgroup of patients with coronary artery disease (CAD) (prior MI, revascularization, or ≥70% coronary stenosis). ResultsACC Pathway assessments were complete in 14,395 patients, of whom 51.7% (7,437 of 14,395) were women with a median age of 56 years (Q1-Q3: 44-68 years). Known CAD was present in 23.5% (3,386 of 14,395) and 30-day death or MI occurred in 8.1% (1,168 of 14,395). The ACC Pathway had an efficacy of 48.1% (95% CI: 47.3%-49.0%). Among patients in the rule-out zone, 0.3% (22 of 6,930) had death or MI at 30 days, yielding a negative predictive value of 99.7% (95% CI: 99.5%-99.8%). In patients with known CAD, 20.0% (676 of 3,386) were classified to the rule-out zone, of whom 1.5% (10 of 676) had death or MI. ConclusionsThe ACC expert consensus decision pathway was safe and efficacious. However, it may not be safe for use among patients with known CAD.

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.