Mortality Risk Assessment Research Articles

Patient-specific multifactorial mortality risk assessment using classification and regression tree analysis in the context of ambulatory blood pressure monitoring.

Ambulatory blood pressure monitoring is essential for understanding blood pressure patterns beyond clinical visits, aiding in risk assessment, treatment evaluation, and managing hypertension. This retrospective cohort study aimed to identify risk factors for all-cause mortality and major cardiovascular events in patients who underwent ambulatory blood pressure monitoring. Eligible participants aged 18 or older, with an estimated glomerular filtration rate (eGFR) > 60ml/min/1.73m2, who underwent ambulatory blood pressure monitoring for various reasons, were included in the study. Data were gathered through telephone interviews, electronic health records, and the national health record system. Descriptive analysis and classification and regression tree modeling were used to uncover significant risk factors related to all-cause mortality and cardiovascular events, and to assess the model's performance compared to traditional Cox survival analysis. The study included 1291 patients, primarily male (51.8%) with a mean age of 61.1 ± 15.2years. During a mean follow-up of 46.9months, 76 (5.9%) patients died of any cause, and 195 (15.1%) had a cardiovascular event. The highest survival rates were observed in patients with a diastolic blood pressure (BP) dipping percentage between -2% and 29%, nighttime systolic BP variability below 32mmHg, and age below 72. Conversely, smokers with a diastolic BP dipping percentage below -10% showed the lowest survival rates. The best cardiovascular outcomes were observed in patients with diastolic BP dipping above -11%, nighttime mean systolic BP < 144mmHg, no statin use, normotensive status, and daytime mean heart rate ≥ 60bpm. Conversely, the worst outcomes were seen in patients with diastolic BP dipping below -11% and a morning surge ≥ 14mmHg. In all-cause mortality and cardiovascular event analysis, the combined model demonstrated excellent calibration and predictive power, like the classification and regression tree model and traditional analysis. These findings highlight the potential of a combined model for assessing mortality and cardiovascular event risk in patients who have undergone ambulatory blood pressure monitoring.

Development of a risk score for predicting one-year mortality in patients with atrial fibrillation using XGBoost-assisted feature selection.

There are no tools specifically designed to assess mortality risk in patients with atrial fibrillation (AF). This study aimed to utilize machine learning methods to identify pertinent variables and develop an easily applicable prognostic score to predict 1-year mortality in AF patients. This study, based on the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database, focused on patients aged 18 years and older with AF. A critical care database from China was the external validation set. The importance of variables from XGBoost guided the development of a logistic model, forming the basis for an AF scoring model. Records of of 26 365 AF patients were obtained from the MIMIC-IV database. The external validation dataset included 231 AF patients. The CRAMB score (Charlson comorbidity index, readmission, age, metastatic solid tumor, and maximum blood urea nitrogen concentration) outperformed the CCI and CHA2DS2-VASc scores, demonstrating superior predictive value for 1-year mortality. In the test set, the area under the receiver operating characteristic (AUC) for the CRAMB score was 0.765 (95% confidence interval [CI], 0.753-0.776), while in the external validation set, it was 0.582 (95% CI, 0.502-0.657). The simplicity of the CRAMB score makes it user-friendly, allowing for coverage of a broader and more heterogeneous AF population.

Proenkephalin improves risk prediction in acute coronary syndromes: derivation and external validation of the KID-ACS risk score

Abstract Background Early assessment of renal and mortality risk in patients with acute coronary syndromes (ACS) is essential to guide treatment decisions according to international guidelines. Circulating proenkephalin (PENK) is a stable opioid metabolite with fast response to changes in kidney function. Purpose This study examined the predictive value of PENK for acute kidney injury (AKI) and mortality in patients presenting with acute coronary syndromes (ACS). Methods Plasma PENK levels were measured using a validated sandwich immunoassay in a prospective multicentre ACS cohort from Switzerland (n = 4 787) and in validation cohorts from the UK (n = 1 141) and Czechia (n = 920). A biomarker-enhanced risk score for simultaneous prediction of both in-hospital acute kidney injury (AKI) and 30-day death (KID-ACS score) with model coefficients adjusted to the outcome was derived and externally validated. In the development of the in-hospital AKI model, patients from one participating study centre were reserved for external validation (Swiss validation cohort). Results Circulating PENK ranked among the top predictors of renal outcomes and mortality in patients with ACS. Accounting for established risk factors and risk models, circulating PENK levels at presentation were independently associated with in-hospital AKI (per log2 increase: adjusted odds ratio [OR] 1.56, 95% CI 1.14 – 2.12, P = 0.005) and with 30-day mortality (per log2 increase: adjusted OR 2.69, 95% CI 1.89 – 3.84, P &amp;lt; 0.001). Dynamic changes in PENK levels within the first 12-24 hours after presentation were strongly and independently associated with in-hospital AKI (increase vs. decrease: adjusted OR 2.83, 95% CI 1.98 – 4.05, P &amp;lt; 0.001). The simple 6-item KID-ACS risk score for in-hospital AKI and 30-day mortality integrates PENK levels at presentation and showed an AUC of 0.72 (95% CI 0.68 – 0.76) for in-hospital AKI, and of 0.91 (95% CI 0.87 – 0.95) for 30-day mortality in the derivation cohort. Upon external validation, KID-ACS had similarly high performance for in-hospital AKI (Swiss validation cohort: AUC 0.73, 95% CI 0.70 – 0.77; Czech validation cohort: 0.74, 95% CI 0.68 – 0.80) and 30-day mortality (UK validation cohort: AUC of 0.87, 95% CI 0.82 – 0.91; Czech validation cohort: 0.91, 95% CI 0.87 – 0.94). The simple 6-item KID-ACS score was non-inferior to established risk models and significantly outperformed the CA-AKI risk score in predicting in-hospital AKI (Zurich cohort: delta AUC 0.11, 95% CI 0.07 – 0.15, P = 0.001) and the GRACE 2.0 in predicting 30-day mortality (UK cohort: delta AUC 0.05, 95% CI 0.01 – 0.09, P = 0.027). Conclusions Circulating PENK was identified as independent predictor of in-hospital AKI and 30-day mortality in patients with ACS. The simple 6-item KID-ACS risk score integrates circulating PENK levels and provides a novel tool for simultaneous assessment of renal and mortality risk in patients presenting with ACS.Independent Predictive Value PENKPerformance and validation of KID-ACS

A novel risk score based on exercise capacity to predict all-cause mortality in adults with congenital heart disease

Abstract Background Impaired exercise capacity in adults with congenital heart disease is associated with mortality. Thus, exercise capacity is a potential tool for predicting risk in this population. The present study attempts to further explore the usefulness of exercise capacity in risk assessment of all-cause mortality, together with a limited number of easily accessible clinical variables. Methods The national register of congenital heart disease was searched for adults (18-60 years of age) with at least one bicycle ergometer exercise test and follow up in the register. The exercise capacity was classified based on percent of predicted exercise capacity (EC%pred); normal (&amp;gt; 70%), moderately impaired (50-70%) or severely impaired (&amp;lt;50%). All patients were followed for 10 years or until death, if occurring earlier than 10 years after the exercise test. Data were analyzed using logistic regression modelling. A risk evaluation model was constructed based on percent of predicted exercise capacity, age, and complexity of the heart lesion. Finally, the model was converted into a 5-point (p) risk score used to predict 10-year mortality (EC%pred &amp;gt;70%=0p, 50-70=1p, &amp;lt;50%=2p, age &amp;lt;25=0p, 25-40=1p, 40-60=2p, simple lesion=0p, moderate/severe complexity of heart lesion=1p). Results 1171 patients (mean age at exercise test 30.5 years, 44% women) were included with 70 deaths occurring within 10 years. A score 0-1 corresponded to &amp;lt;1% 10-year all-cause mortality, 2p 5%, 3p 9%, 4p 21% and 5p 33% mortality risk (Table 1). The area under curve for the model was 0.75 (95% CI 0.69-0.81). Conclusion An easily performed risk assessment containing % of predicted exercise capacity, age and complexity of heart lesion performs well in detecting patients at both low and high risk of all-cause mortality within 10 years after an exercise test.

Value of cardiac enzyme spectrum for the risk assessment of mortality in critically ill children: a single-centre retrospective study

ObjectivesIdentifying high-risk paediatric patients with a poor prognosis and providing timely and adequate treatment are critical. This study aimed to evaluate the effects of different types of cardiac enzyme spectrum...

Machine-Learning Algorithm of Two Continuous Assessment Methods of Dialysis Quality Indicators-Based Prediction Scheme for Assessing Mortality Risk in Patients on Maintenance Hemodialysis

Machine-Learning Algorithm of Two Continuous Assessment Methods of Dialysis Quality Indicators-Based Prediction Scheme for Assessing Mortality Risk in Patients on Maintenance Hemodialysis

A Machine Learning Derived Risk Score Improves Prediction of Outcomes Post-LVAD Implantation: An Analysis of the INTERMACS database.

BackgroundSignificant variability in post-left ventricular assist device (LVAD) implantation outcomes emphasize the importance of accurately assessing patients' risk prior to surgery. This study assesses the MARKER-HF mortality risk model, a machine learning-based tool utilizing 8 clinical variables, to predict post-LVAD implantation mortality and its prognostic enhancement over the INTERMACS profile. MethodsAnalyzing 25,365 INTERMACS database patients (mean age 56.8 years, 78% male), 5,663 (22.3%) and 19,702 (77.7%) received HeartMate 3 and other types of LVAD. They were categorized into low, moderate, high, and very high-risk groups based on MARKER-HF score. The outcomes of interest were in-hospital and 1-year post-discharge mortality. ResultsIn patients receiving HM3 devices, 6.2% died during index hospitalization. In-hospital mortality progressively increased from 4.4% in low to 15.2% in very high -risk groups with MARKER-HF score. MARKER-HF provided additional risk discrimination within each INTERMACS profile. Combining MARKER-HF score and INTERMACS profile identified patients with the lowest (3.5%) and highest in-hospital mortality (19.8%) rates. Post-discharge mortality rate at one year was 5.8% in this population. In a Cox-proportional hazard regression analysis adjusting for both MARKER-HF and INTERMACS profile, only MARKER-HF score (hazard ratio: 1.27, 95% CI: 1.11-1.46, P<0.001) was associated with post-discharge mortality. Similar findings were observed for patients receiving other types of LVAD. ConclusionsThe MARKER-HF score is a valuable tool for assessing mortality risk in HF patients undergoing HM3 and other LVAD implantation. It offers prognostic information beyond that of the INTERMACS profile alone and it's use should help in the shared decision-making process for LVAD implantation.

HIV deathrate prediction by Gaidai multivariate risks assessment method.

HIV is a contagious disease with reportedly high transmissibility, being spread worldwide, with certain mortality, allegedly presenting a burden to public health worldwide. The main objective of this study was to determine excessive HIV death risks at any time within any region or country of interest. Current study presents a novel multivariate public health system bio-risk assessment approach that is particularly applicable to environmental multi-regional, biological, and public health systems, being observed over a representative period of time, yielding reliable long-term HIV deathrate assessment. Hence, the development of a new bio-statistical approach, that is, population-based, multicenter, and medical survey-based. The expansion of extreme value statistics from the univariate to the bivariate situation meets with numerous challenges. Firstly, the univariate extreme value types theorem cannot be directly extended to the bivariate (2D) case, - not to mention challenges with system dimensionality higher than 2D. Existing bio-statistical methods that process spatiotemporal clinical observations of multinational bio-processes often do not have the advantage of efficiently dealing with high regional dimensionalities and complex nonlinear inter-correlations between different national raw datasets. Hence, this study advocates the direct application of the novel bio-statistical Gaidai method to a raw unfiltered clinical data set. This investigation described the successful application of a novel bio-risk assessment approach, yielding reliable long-term HIV mortality risk assessments. The suggested risk assessment methodology may be utilized in various public bio and public health clinical applications based on available raw patient survey datasets.

The illness severity scores can predict the need for respiratory and inotropic support in newborns with critical congenital heart malformations

Background and objectives. The illness severity scoring systems have been developed and validated to assess mortality risks based on clinical conditions and aid critical patient decision-making. Dextro-transposition of the great arteries (D-TGA) and coarctation of the aorta (CoA) are critical congenital heart defects that require medical treatment from birth and surgical repair in the neonatal period. The study aimed to compare the values of three scoring systems in predicting the need for respiratory and inotropic support for newborns with D-TGA and CoA. Materials and methods. An observational study was conducted on inborn infants diagnosed with D-TGA and CoA admitted to the newborn intensive care unit (NICU) within 12 hours of birth at a referral unit for congenital heart disease between January 2018 and December 2020. Newborns with D-TGA and CoA who died within the first 24 hours of birth, outborn infants, and those with a late diagnosis were excluded. For each patient, the Clinical Risk Index of Babies (CRIB-II), Score for Neonatal Acute Physiology-II (SNAP-II), and Score for Neonatal Acute Physiology-Perinatal Extension-II (SNAPPE-II) were calculated. Results. A total of 69 newborns were included in the current study, 30 (43.48 %) of whom were infants with D-TGA (D-TGA Group) and 39 (56.52 %) with CoA (CoA group). Newborns who required respiratory support had significantly higher CRIBII scores (3.03 ± 0.63 vs. 1.34 ± 0.19; p = 0.001), SNAP-II scores (26.61 ± 13.96 vs. 12.43 ± 9.98; p = 0.001), and SNAPPE-II scores (33.04 ± 2.91 vs. 14.47 ± 12.35; p = 0.001) compared to those who did not require it. Significantly higher CRIB-II (3.22 ± 0.67 vs. 1.56 ± 0.23; p = 0.003), SNAP-II (22.91 ±14.57 vs. 14.28 ± 1.58; p = 0.003), and SNAPPE-II (28.82 ± 17.95 vs. 16.58 ± 13.85; p = 0.003) scores were also associated with the need for inotropic support. When comparing the predictive efficiency of illness severity scores for detecting the risk of needing respiratory and inotropic support, they are more effective at predicting the need for ventilation and less effective at predicting the need for inotropes. Conclusions. The illness severity scores can effectively predict the need for pre-surgery interventions. Their simplicity makes them suitable for routine use.

Weight-adjusted waist index is positively and linearly associated with all-cause and cardiovascular mortality in metabolic dysfunction-associated steatotic liver disease: findings from NHANES 1999-2018.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common chronic liver disease. Body mass index (BMI) is the most used obesity index but has important limitations. The weight-adjusted waist index (WWI) is a novel obesity metric and accurately reflects body composition. We explored the association of WWI with all-cause and cardiovascular disease (CVD) mortality in MASLD. Adult participants with MASLD were included from NHANES 1999-2018. WWI was calculated by dividing the waist circumference (WC) by the square root of body weight. MASLD was diagnosed by the presence of hepatic steatosis and at least one cardiometabolic risk factor in the absence of other causes of steatosis. A fatty liver index ≥60 suggested the presence of hepatic steatosis. Mortality data was obtained by prospectively linking to the National Death Index. Multivariate Cox proportional hazards regression analyses were used to explore these associations and multiple adjustment models were constructed including crude, partial, and fully adjusted models. After adjusting for all covariates including BMI, WWI remained positively and linearly associated with all-cause and CVD mortality in MASLD (hazard ratios [HR] 1.247 and 1.218, respectively). Higher WWI was associated with a significantly increased risk of mortality (both p for trend <0.05). There was an "obesity paradox" between BMI and all-cause mortality in MASLD, with significantly lower all-cause mortality in those with overweight/obesity compared to normal BMI (HR 0.625 and 0.596, respectively, p for trend = 0.024), and no association between BMI and CVD mortality. Interaction analyses indicated that these associations were influenced by several demographic variables and disease status. Time-dependent receiver operating characteristic curves indicated that the predictive value of WWI for mortality in MASLD was higher than that of BMI, WC, and waist-to-height ratio across all follow-up durations. WWI was positively and linearly associated with all-cause and CVD mortality in MASLD, whereas BMI did not accurately reflect mortality risk. WWI provided the optimal predictive value for mortality compared to traditional obesity indicators. These findings emphasize the potential use of WWI as a novel obesity indicator for mortality risk assessment, stratification, and prevention in MASLD.

The effectiveness of COVID-19 vaccination in preventing hospitalisation and mortality: A nationwide cross-sectional study in Iran.

The pandemic of the coronavirus disease 2019 (COVID-19) led to a global health crisis, prompting widespread vaccination efforts to reduce severe outcomes. In this study, we assessed the impact of mass COVID-19 vaccination on hospitalisation and mortality rates in Iran, where over 83% of the vaccinated population received inactivated virus vaccines. Using retrospective, cross-sectional analysis, we examined data from the Iran Health Insurance Organisation, covering 41 million individuals from 20 February 2020 to 20 March 2022. We analysed hospital records from 956 Iranian hospitals, focusing on inpatient stays, short-term hospitalisations, and emergency department visits. Study outcomes included COVID-19 hospital admissions and associated mortality. We used negative binomial regression to compare hospital admission rates between periods, while we used a poison regression model with a log link to assess mortality risks before and after vaccination. Among 806 076 hospital admissions, 57 599 deaths were recorded. COVID-19 hospitalisations increased with age, and women had slightly higher admission rates than men. Advanced age and male sex correlated with higher mortality rates. Hospital admissions rose to 1178.66 per million population per month post-vaccination compared to 459.78 pre-vaccination. The incidence rate ratio was 2.09 (95% confidence interval (CI) = 1.90-2.32, P < 0.001), mainly due to the Delta variant. In contrast, post-vaccination mortality rates decreased from 111.33 to 51.66 per 1000 admissions per month. Post-vaccination, COVID-19 mortality significantly decreased, with a relative risk being 0.61 (95% CI = 0.60-0.62, P < 0.001) across all age groups and sexes. The Delta variant increased hospital admissions among vaccinated individuals, but widespread vaccination significantly reduced COVID-19-related mortality.

Association between toxicity-index of diquat and in-hospital mortality in patients with acute diquat poisoning: a retrospective cohort study

Background This study investigates the impact of diquat toxicity levels on in-hospital mortality rates among patients with acute diquat poisoning. It aims to clarify the relationship between diquat toxicity scores and the likelihood of death during hospitalization. Methods A retrospective cohort study was conducted on 98 individuals with acute diquat poisoning. Data on post-ingestion time, initial diquat plasma concentration, and clinical outcomes were systematically collected for all participants. The toxicity-index of diquat was calculated based on post-ingestion time and initial diquat plasma concentration. Logistic regression analysis was utilized to assess the association between the toxicity-index of diquat and in-hospital mortality rates, adjusting for potential confounding variables such as age, comorbidities, and treatment interventions. Results The study found that the overall prevalence of in-hospital mortality was 34.7%, with 58.2% in males. The multivariable-adjusted regression coefficient for in-hospital mortality associated with the toxicity-index was 1.09, with a 95% confidence interval (CI) of 1.01–1.17. Subsequent exploratory subgroup analysis indicated that there were no significant interactions (all p values for interaction were >0.05). Conclusions The study found that higher diquat toxicity-index values correlate with increased in-hospital mortality in acute diquat poisoning cases, indicating that the toxicity-index could be a useful biomarker for assessing mortality risk.

Associations between inflammatory burden index, prostate cancer, and mortality among middle-aged and elderly individuals.

Inflammation plays a crucial role in prostate cancer (PCa) progression and mortality. This study aimed to investigate the predictive value of the inflammatory burden index (IBI) and its components for mortality risk among men aged 40 years and older. A total of 7,344 participants from the NHANES 2001-2010 were included. High PCa risk was defined as a %fPSA greater than 25% and a tPSA level less than 4.0 ng/mL. Cox regression and logistic regression analyses were conducted to assess the association between IBI, PCa risk, and mortality. Receiver operating characteristic (ROC) curve analysis and random survival forest (RSF) model were utilized to evaluate the predictive value of IBI and its components for mortality. Elevated IBI levels were significantly associated with an increased risk of all-cause mortality (HR = 1.08 [1.05-1.10]) and cancer mortality (HR = 1.11 [1.07-1.15]). High-risk PCa cases also exhibited elevated mortality risk (all-cause: HR = 1.35 [1.19-1.54]; cancer: HR = 1.65 [1.27-2.14]). Additionally, the combined effect of elevated IBI levels and high PCa risk showed a synergistic impact on mortality outcomes (all-cause: HR = 1.49 [1.27-1.74]; cancer: HR = 1.76 [1.29-2.40]). ROC curve analysis revealed that IBI had the highest AUC for predicting all-cause mortality (AUC = 0.690 at 3 years, 0.622 at 5 years, 0.634 at 10 years, and 0.632 at 15 years) compared to its individual components (CRP, NEU, LYM). RSF analysis highlighted IBI as the most significant predictor of all-cause and cancer mortality. The combined effect of elevated IBI levels and high PCa risk demonstrated a synergistic impact on increased mortality risk among men aged 40 years and older. IBI demonstrated superior predictive performance for mortality outcomes compared to individual inflammatory markers. These findings underscore the potential utility of IBI as a prognostic biomarker for mortality risk assessment in individual with high PCa risk.

Fracture-related infections after osteosynthesis for hip fracture are associated with higher mortality: A retrospective single-center cohort study.

Fracture-related infections (FRIs) after osteosynthesis for hip fractures have not been thoroughly investigated. Our primary aim was to assess the association between FRIs and mortality after osteosynthesis for hip fracture. Secondary aims were to investigate the incidence, microbiology, and general epidemiological aspects of these FRIs. This retrospective single-center study included 1,455 patients > 18 years old with non-pathological hip fractures treated with osteosynthesis between 2015 and 2019. Medical records were reviewed and FRIs were diagnosed based on current consensus criteria. The follow-up period was 2 years. Mortality was estimated using Kaplan-Meier survival analysis. Cox regression analyses were performed to investigate the potential association between FRIs, as a time-dependent variable, and increased mortality. The median age for the entire cohort was 83 (interquartile range 75-89) years and 69% were females. At the 2-year follow-up mark, the crude mortality rate was 33% in the non-FRI group and 69% (11 of 16 patients) in the FRI group. Cox regression analysis assessing mortality risk revealed a hazard ratio of 3.5 (95% confidence interval [CI] 1.9-6.4) when adjusted for confounders. The incidence of FRI was 1.1% (16 of 1,455 patients).Staphylococcus aureuswas the most common pathogen. Most FRI patients (94%) required at least 1 revision and 56% underwent ≥ 2 revision. We found an association between FRIs after hip fracture osteosynthesis and increased mortality, underscoring the critical need for FRI prevention measures in this frail patient group. The incidence and microbiological findings were consistent with previous studies.

Perioperative Care for Bariatric Surgery.

This review will start with a brief pathophysiology of obesity and the requirement for bariatric surgery, and it continues with a preoperative assessment, which includes a surgical mortality risk assessment, respiratory and cardiovascular assessments, and a psychological assessment. In-hospital postoperative care will be discussed, including which patients need a surgical intensive care unit and the monitoring tools required. The need for postoperative medications, postoperative complications, strategies for management, and a follow-up plan are also reviewed. This manuscript is written in a narrative review form with a chance of bias as a possible limitation.

Mortality Risk Assessment Using Deep Learning-Based Frequency Analysis of EEG and EOG in Sleep

Abstract Study Objectives To assess whether the frequency content of electroencephalography (EEG) and electrooculography (EOG) during nocturnal polysomnography (PSG) can predict all-cause mortality. Methods Power spectra from PSGs of 8,716 participants, included from the MrOS Sleep Study and the Sleep Heart Health Study (SHHS), were analyzed in deep learning-based survival models. The best-performing model was further examined using SHapley Additive Explanation (SHAP) for data-driven sleep-stage specific definitions of power bands, which were evaluated in predicting mortality using Cox Proportional Hazards models. Results Survival analyses, adjusted for known covariates, identified multiple EEG frequency bands across all sleep stages predicting all-cause mortality. For EEG, we found an all-cause mortality hazard ratio (HR) of 0.90 (CI95% 0.85–0.96) for 12–15 Hz in N2, 0.86 (CI95% 0.82–0.91) for 0.75–1.5 Hz in N3, and 0.87 (CI95% 0.83–0.92) for 14.75–33.5 Hz in REM sleep. For EOG, we found several low-frequency effects including an all-cause mortality HR of 1.19 (CI95% 1.11–1.28) for 0.25 Hz in N3, 1.11 (CI95% 1.03–1.21) for 0.75 Hz in N1, and 1.11 (CI95% 1.03–1.20) for 1.25–1.75 Hz in Wake. The gain in the concordance index (C-index) for all-cause mortality is minimal, with only a 0.24% increase: The best single mortality predictor was EEG N3 (0-0.5 Hz) with C-index of 77.78% compared to 77.54% for confounders alone. Conclusion Spectral power features, possibly reflecting abnormal sleep microstructure, are associated with mortality risk. These findings add to a growing literature suggesting that sleep contains incipient predictors of health and mortality.

Lower mortality risk associated with remdesivir + dexamethasone versus dexamethasone alone for the treatment of patients hospitalized for COVID-19

Abstract Background Treatment guidelines were developed early in the pandemic when much about COVID-19 was unknown. Given the evolution of SARS-CoV-2, real-world data can provide clinicians with updated information. The objective of this analysis was to assess mortality risk in patients hospitalized for COVID-19 during the Omicron period receiving remdesivir+dexamethasone versus dexamethasone alone. Methods A large, multicenter US hospital database was used to identify hospitalized adult patients, with a primary discharge diagnosis of COVID-19 also flagged as “present on admission” treated with remdesivir+dexamethasone or dexamethasone alone from December 2021 to April 2023. Patients were matched 1:1 using propensity score matching and stratified by baseline oxygen requirements. Cox proportional hazards model was used to assess time to 14- and 28-day in-hospital all-cause mortality. Results A total of 33 037 patients were matched, with most patients ≥65 years old (72%), White (78%), and non-Hispanic (84%). Remdesivir+dexamethasone was associated with lower mortality risk versus dexamethasone alone across all baseline oxygen requirements at 14 days (no supplemental oxygen charges: adjusted hazard ratio [95% CI]: 0.79 [0.72-0.87], low flow oxygen: 0.70 [0.64-0.77], high flow oxygen/non-invasive ventilation: 0.69 [0.62-0.76], invasive mechanical ventilation/extracorporeal membrane oxygen (IMV/ECMO): 0.78 [0.64-0.94]), with similar results at 28 days. Conclusions Remdesivir+dexamethasone was associated with a significant reduction in 14- and 28-day mortality compared to dexamethasone alone in patients hospitalized for COVID-19 across all levels of baseline respiratory support, including IMV/ECMO. However, the use of remdesivir+dexamethasone still has low clinical practice uptake. In addition, these data suggest a need to update the existing guidelines.

Association of inflammation/nutrition-based indicators with Parkinson's disease and mortality.

The study explores the association between inflammation/nutrition-based indicators, Parkinson's disease (PD), and all-cause mortality among adult participants. The analysis included 38,091 participants from National Health and Nutrition Examination Survey (NHANES) 1999-2018. Inflammation/nutrition-based indicators were derived from a comprehensive set of parameters, including neutrophil-albumin ratio (NAR), prognostic nutritional index (PNI), monocyte-albumin ratio (MAR), red cell distribution width-albumin ratio (RAR), hemoglobin, albumin, lymphocyte, and platelet (HALP) score, advanced lung cancer inflammation index (ALI), geriatric nutrition risk index (GNRI), and controlling nutritional status (CONUT) score. PD status was determined based on self-reported anti-parkinsonian medication use. Mortality data were obtained from the National Death Index, linked up to December 30, 2019. After multivariate adjustment, all inflammation/nutrition-based indicators showed significant associations with all-cause mortality among adult participants. The random survival forest emphasized the importance of inflammation/nutrition-based indicators and their components in predicting mortality, with PNI and RAR being the most important indicators for predicting all-cause mortality. Individuals with PD have a significantly higher risk of all-cause mortality compared to those without PD (HR = 1.747 [1.363-2.238], P < 0.001) among adults. Additionally, elevated levels of inflammation/nutrition-based indicators are associated with a higher risk of all-cause mortality among individuals with PD compared to those without PD. This suggested a synergistic effect of PD and elevated levels of inflammatory/nutritional indicators on mortality risk. Specifically, individuals with PD and elevated NAR (HR = 2.066 [1.398-3.052], P < 0.001), MAR (HR = 2.249 [1.612-3.138], P < 0.001), RAR (HR = 1.617 [1.179-2.218], P = 0.003), ALI (HR = 1.763 [1.225-2.537], P = 0.002) and CONUT (HR = 2.221 [1.434-3.440], P < 0.001), and not elevated PNI (HR = 1.771 [1.295-2.423], P < 0.001), HALP (HR = 1.738 [1.242-2.432], P = 0.001), and GNRI (HR = 2.689 [1.898-3.811], P < 0.001) have a substantially higher risk of mortality compared to those without PD. Inflammation and nutrition status play crucial roles in predicting all-cause mortality among adults, particularly in the context of PD. The study underscores the importance of considering both factors in mortality risk assessment and provides valuable insights for future research and clinical practice.

Interpretable mortality prediction model for ICU patients with pneumonia: using shapley additive explanation method

BackgroundPneumonia, a leading cause of morbidity and mortality worldwide, often necessitates Intensive Care Unit (ICU) admission. Accurate prediction of pneumonia mortality is crucial for tailored prevention and treatment plans. However, existing mortality prediction models face limited adoption in clinical practice due to their lack of interpretability.ObjectiveThis study aimed to develop an interpretable model for predicting pneumonia mortality in ICUs. Leveraging the Shapley Additive Explanation (SHAP) method, we sought to elucidate the Extreme Gradient Boosting (XGBoost) model and identify prognostic factors for pneumonia.MethodsConducted as a retrospective cohort study, we utilized electronic health records from the eICU-CRD (2014–2015) for all adult pneumonia patients. The first 24 h of each ICU admission records were considered, with 70% of the dataset allocated for model training and 30% for validation. The XGBoost model was employed, and performance was assessed using the area under the receiver operating characteristic curve (AUC). The SHAP method provided insights into the XGBoost model.ResultsAmong 10,962 pneumonia patients, in-hospital mortality was 16.33%. The XGBoost model demonstrated superior predictive performance (AUC: 0.778 ± 0.016)) compared to traditional scoring systems and other machine learning method, which achieved an improvement of 10% points. SHAP analysis identified Aspartate Aminotransferase (AST) as the most crucial predictor.ConclusionsInterpretable predictive models enhance mortality risk assessment for pneumonia patients in the ICU, fostering transparency. AST emerged as the foremost predictor, followed by patient age, albumin, BMI et al. These insights, rooted in strong correlations with mortality, facilitate improved clinical decision-making and resource allocation.

A nomogram for predicting short-term mortality in ICU patients with coexisting chronic obstructive pulmonary disease and congestive heart failure

ObjectiveThis study aimed to develop and validate a nomogram for predicting 28-day and 90-day mortality in intensive care unit (ICU) patients who have chronic obstructive pulmonary disease (COPD) coexisting with congestive heart failure (CHF). MethodsAn extensive analysis was conducted on clinical data from the Medical Information Mart for Intensive Care IV database, covering patients over 18 years old with both COPD and CHF, who were were first-time ICU admissions between 2008 and 2019. The least absolute shrinkage and selection operator (LASSO) regression method was employed to screen clinical features, with the final model being optimized using backward stepwise regression guided by the Akaike Information Criterion (AIC) to construct the nomogram. The predictive model's discrimination and clinical applicability were evaluated via receiver operating characteristic (ROC) curves, calibration curves, the C-index, and decision curve analysi s (DCA). ResultsThis analysis was comprised of a total of 1948 patients. Patients were separated into developing and validation cohorts in a 7:3 ratio, with similar baseline characteristics between the two groups. The ICU mortality rates for the developing and verification cohorts were 20.8 % and 19.5 % at 28 days, respectively, and 29.4 % and 28.3 % at 90 days, respectively. The clinical characteristics retained by the backward stepwise regression include age, weight, systolic blood pressure (SBP), respiratory rate (RR), oxygen saturation (SpO2), red blood cell distribution width (RDW), lactate, partial thrombosis time (PTT), race, marital status, type 2 diabetes mellitus (T2DM), malignant cancer, acute kidney failure (AKF), pneumonia, immunosuppressive drugs, antiplatelet agents, vasoactive agents, acute physiology score III (APS III), Oxford acute severity of illness score (OASIS), and Charlson comorbidity index (CCI). We developed two separate models by assigning weighted scores to each independent risk factor: nomogram A excludes CCI but includes age, T2DM, and malignant cancer, while nomogram B includes only CCI, without age, T2DM, and malignant cancer. Based on the results of the AUC and C-index, this study selected nomogram A, which demonstrated better predictive performance, for subsequent validation. The calibration curve, C-index, and DCA results indicate that nomogram A has good accuracy in predicting short-term mortality and demonstrates better discriminative ability than commonly used clinical scoring systems, making it more suitable for clinical application. ConclusionThe nomogram developed in this study offers an effective assessment of short-term mortality risk for ICU patients with COPD and CHF, proving to be a superior tool for predicting their short-term prognosis.