Predictors Of Hospital Mortality Research Articles

The GCS-Pupils (GCS-P) score to assess outcomes after traumatic brain injury: a retrospective study

Introduction The Glasgow Coma Scale (GCS) and pupil response to light are commonly used to assess brain injury severity and predict outcomes. The aim of this study was to investigate whether the GCS combined with pupil response (GCS-P), compared to the GCS alone, could be a better predictor of hospital mortality for patients with traumatic brain injury (TBI). Methods A retrospective cohort study was undertaken at an adult level one trauma centre including patients with isolated TBI of Abbreviated Injury Scale above three. The GCS and pupil response were combined to an arithmetic score (GCS score (range 3–15) minus the number of nonreacting pupils (0, 1, or 2)), or by treating each factor as separate categorical variables. The association of in-hospital mortality with GCS-P as a categorical variable was evaluated using Nagelkerke’s R 2 and compared using areas under the receiver operating characteristic (AUROC) curve. Results There were 392 patients included over the study period of 1 July 2014 and 30 September 2017, with an overall mortality rate of 15.2%. Mortality was highest at GCS-P of 1 (79%), with lowest mortality at a GCS-P 15 (1.6%). Nagelkerke’s R 2 was 0.427 for GCS alone and 0.486 for GCS-P. The AUROC for GCS-P to predict mortality was 0.87 (95%CI: 0.82–0.72), higher than for GCS alone (0.85; 95%CI: 0.80–0.90; p < .001). Discussion GCS-P provided a better predictor of mortality compared to the GCS. As both the GCS and pupillary response are routinely recorded on all patients, combination of these pieces of information into a single score can further simplify assessment of patients with TBI, with some improvement in performance.

Alpha-1-antitrypsin-deficiency is associated with lower cardiovascular risk: an approach based on federated learning

BackgroundChronic obstructive pulmonary disease (COPD) is an inflammatory multisystemic disease caused by environmental exposures and/or genetic factors. Inherited alpha-1-antitrypsin deficiency (AATD) is one of the best recognized genetic factors increasing the risk for an early onset COPD with emphysema. The aim of this study was to gain a better understanding of the associations between comorbidities and specific biomarkers in COPD patients with and without AATD to enable future investigations aimed, for example, at identifying risk factors or improving care.MethodsWe focused on cardiovascular comorbidities, blood high sensitivity troponin (hs-troponin) and lipid profiles in COPD patients with and without AATD. We used clinical data from six German University Medical Centres of the MIRACUM (Medical Informatics Initiative in Research and Medicine) consortium. The codes for the international classification of diseases (ICD) were used for COPD as a main diagnosis and for comorbidities and blood laboratory data were obtained. Data analyses were based on the DataSHIELD framework.ResultsOut of 112,852 visits complete information was available for 43,057 COPD patients. According to our findings, 746 patients with AATD (1.73%) showed significantly lower total blood cholesterol levels and less cardiovascular comorbidities than non-AATD COPD patients. Moreover, after adjusting for the confounder factors, such as age, gender, and nicotine abuse, we confirmed that hs-troponin is a suitable predictor of overall mortality in COPD patients. The comorbidities associated with AATD in the current study differ from other studies, which may reflect geographic and population-based differences as well as the heterogeneous characteristics of AATD.ConclusionThe concept of MIRACUM is suitable for the analysis of a large healthcare database. This study provided evidence that COPD patients with AATD have a lower cardiovascular risk and revealed that hs-troponin is a predictor for hospital mortality in individuals with COPD.

Comparison of early warning and sepsis scores for mortality prediction in patients with suspected infection admitted to medical intensive care units

Aims: To compare the mortality prediction efficiency of the Modified Early Warning Score (MEWS), Systemic Inflammatory Response Syndrome (SIRS), Sepsis Related Organ Failure Assessment (SOFA), and Quick Sepsis Related Organ Failure Assessment (qSOFA) calculated within 48 hours before ICU admission. Methods: A prospective, noninterventional, observational cohort study enrolled adult patients admitted to medical intensive care units (ICU) with suspected infection in a tertiary care medical center. MEWS SIRS, SOFA, and qSOFA scores were calculated at four different time points: 48, 24, and 8 hours before and at the time of the ICU admission (0. hour). The scores were analyzed for hospital mortality. Results: A total of 120 patients were included. The median age was 68 (IQR 59.8-79) years, and 44.2% of patients were male. Of the study population, 75.8% were admitted to the medical ICU from the emergency department, while the remaining were from the medical wards. Considering the scores observed 48 hours before ICU admission, Odds Ratio (OR) of SIRS≥2 and SOFA≥2 showed a value of 7.6 (95% CI: 1.5-38.0) and 13.2 (95% CI: 2.3-74.3), respectively, while no increase in risk was observed for MEWS and the qSOFA score. Receiver operating characteristic analysis (ROC) performed with the highest scores observed at any time within 48 hours before ICU admission (ICU admission values were omitted) regarding hospital mortality yielded area under the curve (AUC) values (95% CI) of 0.80 (0.72-0.89) for SOFA, 0.66 (0.54-0.76) for MEWS, 0.63 (0.51-0.74) for qSOFA, and 0.61 (0.49-0.73) for SIRS. SOFA had the highest sensitivity of 92.6% (82.7-100.0), whereas qSOFA had the highest specificity of 63.0% (49.1-77.0) for hospital mortality. Conclusion: SOFA score is the most sensitive scoring system to predict hospital mortality in patients admitted to the medical ICU with suspected infection compared to MEWS, SIRS, and qSOFA. Nevertheless, the sepsis and early warning scores should be combined in clinical practice whenever possible.

Clinical characteristics and mortality prediction of patients admitted to the Hong Kong East Cluster intensive care units in the COVID‐19 fifth wave

AbstractIntroductionCOVID‐19 has been threatening global public health since 2019. To address the overwhelming caseload, several tools were developed to predict prognosis and aid triage of critically ill patients for intensive care. Currently, there is a lack of local data on the validity of such tools.ObjectiveThe objective of this study is to identify the predictors of intensive care unit (ICU) mortality in COVID‐19 patients in Hong Kong and externally validate the different scoring systems.MethodsA retrospective cohort analysis of patients admitted to the HKEC ICUs from 1 January 2022 to 30 April 2022 was performed. We collected data on patient demographics, vaccination status, laboratory parameters, and clinical outcomes including need for organ support and mortality. Clinical severities were estimated by Sequential Organ Failure Score, 4C Mortality Score, COVID‐Gram score, and Acute Physiology and Chronic Health Evaluation (APACHE) IV score based on the original studies. Comparison between individual scoring systems' performance on hospital mortality was conducted and summarized.ResultsIn these four months, 137 patients with COVID‐19 admitted to ICUs of Ruttonjee Hospital and Pamela Youde Nethersole Eastern Hospital were recruited. 64 patients (46.7%) were admitted for COVID‐19 related respiratory failure, among which mortality was 66.7%. The overall hospital and ICU mortality were 21.9% and 13.1%, respectively. Invasive mechanical ventilation (IMV) (OR 3.221, p 0.034), high flow nasal cannula (HFNC) oxygen therapy (OR 3.107, p 0.039), and higher APACHE IV score (OR 1.043, p &lt; 0.001) were independent predictors of hospital mortality using multivariate analysis. The scoring systems had good performance in mortality prediction in our population. The APACHE IV score (AUROC 0.79, 95% CI 0.698–0.894) and 4C Mortality Score (AUROC 0.751, 95% CI 0.657–0.844) outperformed other scoring systems in predicting hospital mortality.ConclusionIn patients with COVID‐19, the use of IMV or HFNC and APACHE IV score were independent risk factors for hospital mortality. The APACHE IV and the 4C Mortality Score were most useful in our population for predicting ICU and hospital mortality.

Comparative analysis of in-hospital and long-term results of patients with acute dysfunction of coronary bypass grafts depending on treatment tactics

Comparative analysis of in-hospital and long-term mortality of patients in whom acute dysfunction of coronary bypass grafts was detected in the early postoperative period depending on conservative or surgical tactics. The study is a retrospective analysis of data from 8801 patients who underwent elective coronary artery bypass grafting (CABG) between 2011 and 2022 at the Federal Center for Cardiovascular Surgery (Russia, Chelyabinsk). Among them, 196 patients underwent emergency coronary artery bypass grafting due to suspected perioperative myocardial infarction in the early postoperative period. In 119 patients, dysfunction of coronary bypass grafts was detected, in 77 patients no pathological changes were found. The criteria for inclusion in the study were patients with dysfunction of coronary bypass grafts (n=119). The 1st group included patients who underwent conservative therapy (n=33), the 2nd group (n=86) included those who underwent repeated myocardial revascularization. The primary endpoint was hospital mortality, secondary endpoints were long-term mortality and adverse cardiovascular events (myocardial infarction, stroke, repeat myocardial revascularization). Patients were surveyed via telephone. In-hospital mortality in the group of surgical reintervention was 8.1%, in the group of conservative treatment - 9.1% (p=0.867). According to the results of multivariate analysis, predictors of hospital mortality in patients of both groups were extracorporeal membrane oxygenation (p=0.014), time of artificial circulation (p=0.031), duration of artificial ventilation (p=0.001), number of days in intensive care (p<0.001). When analyzing long-term mortality using the Kaplan-Meier method in group 1 and group 2, no statistically significant differences were found; in the group of conservative therapy - 85±9.6 [66.2-103.7] months versus 108.2±4.8 [98.8-117.6] months in the surgery group (log-rank p=0.06). When analyzing long-term mortality from cardiovascular causes and the occurrence of adverse cardiovascular events, statistically significant differences were determined: in the group of conservative therapy - 92.5±9.3 [74.2-110.7] months versus 117.8±3.3 [111.2-124.3] months in the surgical treatment group (log-rank p=0.007) and 78.1±9.2 [60-96.3] months versus 98.9±3.9 [91.3-106.5] months (log-rank p=0.008), respectively. In-hospital mortality was comparable between groups. Long-term mortality from cardiovascular causes and the number of adverse cardiovascular events were significantly higher in the conservative therapy group. With timely detection of acute dysfunction of coronary bypass grafts, an active surgical approach has an advantage over conservative tactics and can improve the long-term prognosis of patients.

Presepsin as a predictor of septic shock and mortality in patients with urinary tract infection according to the Sepsis-3 definitions

The objective of this study was to investigate whether presepsin can serve as a useful biomarker for predicting septic shock and mortality rates in patients with urinary tract infections (UTI) as defined by the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). This single-center and prospective observational study was carried out between December 2019 and December 2022 and included 171 UTI patients divided into two groups: a non-septic shock group (n = 121) and a septic shock group (n = 50). The primary outcome of this study was the development of septic shock; the secondary outcome was 30-day hospital mortality. Receiver operating characteristic (ROC) curves and multivariate regression analyses were performed to investigate the predictive value of presepsin levels for septic shock and the Cox proportional hazards model was used to determine the risk factors for 30-day hospital mortality in UTI patients. Septic shock patients had significantly higher serum levels of presepsin when compared to the non-septic shock group (p &lt; 0.001). In multivariate logistic regression analysis, presepsin demonstrated its independent role as a risk factor for septic shock (odds ratio (OR): 1.002; 95% confidence interval (CI): 1.001–1.002; p &lt; 0.001). The multivariate Cox proportional hazards model indicated that presepsin represents a significant predictor of 30-day hospital mortality in septic shock patients (hazard ratio (HR): 1.0005; 95% CI: 1.0001–1.001; p &lt; 0.05). The ROC curve for diagnosing septic shock had an area under the curve (AUC) of 0.739 with a cutoff value of 447 pg/mL for presepsin. For the prediction of 30-day hospital mortality in patients with UTI, an optimal presepsin cutoff of 709 pg/mL was determined; ROC curve analysis yielded an AUC of 0.744. When applying the Sepsis-3 criteria, presepsin levels represented a significant independent risk factor for septic shock and 30-day hospital mortality in UTI patients.

Machine learning-based risk prediction of acute kidney disease and hospital mortality in older patients.

Acute kidney injury (AKI) is a prevalent complication in older people, elevating the risks of acute kidney disease (AKD) and mortality. AKD reflects the adverse events developing after AKI. We aimed to develop and validate machine learning models for predicting the occurrence of AKD, AKI and mortality in older patients. We retrospectively reviewed the medical records of older patients (aged 65 years and above). To explore the trajectory of kidney dysfunction, patients were categorized into four groups: no kidney disease, AKI recovery, AKD without AKI, or AKD with AKI. We developed eight machine learning models to predict AKD, AKI, and mortality. The best-performing model was identified based on the area under the receiver operating characteristic curve (AUC) and interpreted using the Shapley additive explanations (SHAP) method. A total of 22,005 patients were finally included in our study. Among them, 4,434 patients (20.15%) developed AKD, 4,000 (18.18%) occurred AKI, and 866 (3.94%) patients deceased. Light gradient boosting machine (LGBM) outperformed in predicting AKD, AKI, and mortality, and the final lite models with 15 features had AUC values of 0.760, 0.767, and 0.927, respectively. The SHAP method revealed that AKI stage, albumin, lactate dehydrogenase, aspirin and coronary heart disease were the top 5 predictors of AKD. An online prediction website for AKD and mortality was developed based on the final models. The LGBM models provide a valuable tool for early prediction of AKD, AKI, and mortality in older patients, facilitating timely interventions. This study highlights the potential of machine learning in improving older adult care, with the developed online tool offering practical utility for healthcare professionals. Further research should aim at external validation and integration of these models into clinical practice.

Prognostic Value of Neutrophils-to-Lymphocytes Ratio and Platelets-to-Lymphocytes Ratio in Sepsis Patients With Lymphopenia.

Inflammation plays a critical role in sepsis. The integration of neutrophil-to-lymphocyte ratio (NLR) and platelets-to-lymphocytes ratio (PLR) from multiple cell types offers a novel approach to rapidly assess inflammation status. However, the predictive role of NLR and PLR in sepsis with lymphopenia remains uncertain. The purpose of this study was to explore the prognostic value of NLR and PLR in sepsis patients with lymphopenia. In this observational retrospective study, we included 172 sepsis patients with lymphopenia and collected clinical characteristics for analysis. Through binary logistic regression analysis, we identified independent factors. Receiver-operating characteristic curves (ROC) and areas under the curves (AUC) were employed to assess the ability to predict hospital mortality risk. Our results showed a total hospital mortality rate of 53.49%. Multivariate analysis demonstrated that NLR (OR = 1.11, P < .001) and PLR (OR = 1.01, P = .003) were independent predictors associated with hospital mortality in sepsis patients with lymphopenia. The AUCs of NLR and PLR were 0.750 (95% CI: 0.634-0.788, P < .001) and 0.662 (95% CI: 0.580-0.743, P < .001), respectively. Notably, an optimal cut-off value of 18.93 for NLR displayed a sensitivity of 75.0% and specificity of 63.0% in discriminating hospital mortality in sepsis patients with lymphopenia, while the optimal cut-off value for PLR was 377.50, with a sensitivity of 67.5% and specificity of 64.1%. NLR and PLR serve as independent predictors of hospital mortality in sepsis patients with lymphopenia.

Hyperglycemia and elevated C-reactive protein are independent predictors of hospital mortality in hospitalized COVID-19 patients in South-Kivu, eastern Democratic Republic of the Congo: A cross-sectional study.

The coronavirus disease 2019 (COVID-19) pandemic was a priority public health problem because of its high mortality rate. This study mainly aimed to determine factors associated with a poor outcome in COVID-19 hospitalized patients in South-Kivu, an eastern province of the Democratic Republic of the Congo (DRC). This observational study retrospectively evaluated medical records of patients consecutively admitted for probable or confirmed COVID-19 between May 01 and July 31, 2020 at the Hôpital Provincial Général de Référence de Bukavu (HPGRB), a tertiary hospital located in South-Kivu. A binary logistic regression model was performed to determine the predictors of mortality. A total of 157 hospitalized COVID-19 patients aged 57.7 (13.2) years were included in this study. Male gender (69.4%), older age (52.9%), medical history of diabetes (38.2%), and arterial hypertension (35.1%) were the most frequent risk factors. Most patients presented with fever (73.3%), cough (72.6%), and dyspnea (66.2%). Overall, 45.1% of patients died. Intrahospital mortality was significantly associated with advanced age [odds ratio, OR (95% confidence interval, CI) = 2.34 (1.06-5.38)], hypoxemia [OR (95% CI) = 4.67 (2.02-10.77)], hyperglycemia [OR (95% CI) = 2.14 (1.06-4.31)], kidney failure [OR (95% CI) = 2.82 (1.4-5.68)], hyperleukocytosis [OR (95% CI) = 3.33 (1.67-6.66)], and higher C-reactive protein (CRP) levels [OR (95% CI) = 3.93 (1.93-8.01)]. After adjustment for various covariates, only higher CRP levels [OR (95% CI) = 3.23 (1.23-8.5)] and hyperglycemia [OR (95% CI) = 2.5 (1.02-6.11)] at admission were independently associated with mortality. Hyperglycemia and marked inflammatory syndrome were the major predictors of poor outcomes in patients hospitalized for COVID-19 in South-Kivu. These two factors should be quantified at hospital admission to establish the patient's prognosis.

Validating the performance of organ dysfunction scores in children with infection: A cohort study.

We aimed to validate the performance of six available scoring models for predicting hospital mortality in children with suspected or confirmed infections. This single-center retrospective cohort study included pediatric patients admitted to the PICU for infection. The primary outcome was hospital mortality. The six scores included the age-adapted pSOFA score, SIRS score, PELOD2 score, Sepsis-2 score, qSOFA score, and PMODS. Of the 5,356 children admitted to the PICU, 9.1% (488) died, and 25.1% (1,342) had basic disease with a mortality rate of 12.7% (171); 65.3% (3,499) of the patients were younger than 2 years, and 59.4% (3,183) were male. The discrimination abilities of the pSOFA and PELOD2 scores were superior to those of the other models. The calibration curves of the pSOFA and PELOD2 scores were consistent between the predictions and observations. Elevated lactate levels were a risk factor for mortality. The pSOFA and PELOD2 scores had superior predictive performance for mortality. Given the relative unavailability of items and clinical operability, the pSOFA score should be recommended as an optimal tool for acute organ dysfunction in pediatric sepsis patients. Elevated lactate levels are related to a greater risk of death from infection in children in the PICU.

Performance of the Risk Scores for Predicting In-Hospital Mortality in Patients with Acute Coronary Syndrome in a Chinese Cohort.

The prognosis of patients with acute coronary syndrome (ACS) varies greatly, and risk assessment models can help clinicians to identify and manage high-risk patients. While the Global Registry of Acute Coronary Events (GRACE) model is widely used, the clinical pathways for acute coronary syndromes (CPACS), which was constructed based on the Chinese population, and acute coronary treatment and intervention outcomes network (ACTION) have not yet been validated in the Chinese population. Patients with ACS who underwent coronary angiography or percutaneous coronary intervention from 2011 to 2020, were retrospectively recruited and the appropriate corresponding clinical indicators was obtained. The primary endpoint was in-hospital mortality. The performance of the GRACE, GRACE 2.0, ACTION, thrombolysis in myocardial infarction (TIMI) and CPACS risk models was evaluated and compared. A total of 19,237 patients with ACS were included. Overall, in-hospital mortality was 2.2%. ACTION showed the highest accuracy in predicting discriminated risk (c-index 0.945, 95% confidence interval [CI] 0.922-0.955), but the calibration was not satisfactory. GRACE and GRACE 2.0 did not significantly differ in discrimination (p = 0.1480). GRACE showed the most accurate calibration in all patients and in the subgroup analysis of all models. CPACS (c-index 0.841, 95% CI 0.821-0.861) and TIMI (c-index 0.811, 95% CI 0.787-0.835) did not outperform (c-index 0.926, 95% CI 0.911-0.940). In contemporary Chinese ACS patients, the ACTION risk model's calibration is not satisfactory, although outperformed the gold standard GRACE model in predicting hospital mortality. The CPACS model developed for Chinese patients did not show better predictive performance than the GRACE model.

Relationship of Hospital Mortality with Metabolic Acidosis Severity in Critically Ill Patients

Background: Metabolic acidosis (MA) is a common pathologic process with fatal consequences in critically ill (CI) patients. The more the severity of acidosis the more mortality rate is expected. To evaluate the relationship of hospital mortality with MA severity in CI patients admitted to emergency department (ED).&#x0D; Methods: In this prospective cohort study, we enrolled CI patients (based on physician clinical assumption), most at level 1 or 2 emergency severity index triage system. Patients were followed and evaluated in 2 parallel groups, one with and the other without MA. The severity of acidosis, chief complaints, final diagnosis, demographic data, acute physiologic assessment and chronic health evaluation II (APACHE II) score, serum lactate and bicarbonate level, need for intubation and mechanical ventilation, admission ward, hospital length of stay and in hospital mortality were compared between the 2 groups.&#x0D; Results: A total of 1811 CI patients including 60.2% males and 39.8% females with and without MA were evaluated. The most common age range was 65-55 years old (31.7%) with the meanSD of 61.348.23. The most common complaints and diagnoses were weakness (40.5%) and pneumosepsis (35.1%), respectively. Patients with severe acidosis had higher lactate level and APACHE II score (p&lt;0.05). Mortality rate was 10.4%. Most of our cases had severe MA. Expired cases had higher lactate level and APACHE II score (p&lt;0.05).&#x0D; Conclusion: Lactate level, bicarbonate level and APACHE II score were all significant independent predictors of hospital mortality in CI patients.

Adjusting Acute Kidney Injury Kidney Disease: Improving Global Outcomes Urine Output Criterion for Predicted Body Weight Improves Prediction of Hospital Mortality.

Based on the Kidney Disease: Improving Global Outcomes (KDIGO) definitions, urine output, serum creatinine, and need for kidney replacement therapy are used for staging acute kidney injury (AKI). Currently, AKI staging correlates strongly with mortality and can be used as a predictive tool. However, factors associated with the development of AKI may affect its predictive ability. We tested whether adjustment for predicted (versus actual) body weight improved the ability of AKI staging to predict hospital mortality. A total of 3279 patients who had undergone cardiac surgery in a university hospital were retrospectively analyzed. AKI was staged according to KDIGO criteria (standard staging) and after adjustment for hourly urine output adjusted by predicted body weight for each patient and each day of their hospital stay. The incidence of AKI (all stages) was 43% (predicted body weight adjusted) and 50% (standard staging), respectively (P < .001). In sensitivity-specificity analyses for predicting hospital mortality, the area under the curve was significantly higher after adjustment for predicted body weight than with standard staging (P = .002). Compared to standard staging, adjustment of urine output for predicted body weight increases the specificity and improves prediction of hospital mortality in patients undergoing cardiac surgery.

Congenital Heart Surgery Machine Learning-Derived In-Depth Benchmarking Tool

BackgroundWe previously showed that machine learning-based methodologies of optimal classification trees (OCTs) can accurately predict risk after congenital heart surgery and assess case-mix–adjusted performance after benchmark procedures. We extend this methodology to provide interpretable, easily accessible, and actionable hospital performance analysis across all procedures. MethodsThe European Congenital Heart Surgeons Association Congenital Cardiac Database data subset of 172,888 congenital cardiac surgical procedures performed in European centers between 1989 and 2022 was analyzed. OCT models (decision trees) were built predicting hospital mortality (area under the curve [AUC], 0.866), prolonged postoperative mechanical ventilatory support time (AUC, 0.851), or hospital length of stay (AUC, 0.818), thereby establishing case-adjusted benchmarking standards reflecting the overall performance of all participating hospitals, designated as the “virtual hospital.” OCT analysis of virtual hospital aggregate data yielded predicted expected outcomes (both aggregate and for risk-matched patient cohorts) for the individual hospital’s own specific case-mix, readily available on-line. ResultsRaw average rates were hospital mortality, 4.9%; mechanical ventilatory support time, 14.5%; and length of stay, 15.0%. Of 146 participating centers, compared with each hospital’s overall case-adjusted predicted hospital mortality benchmark, 20.5% statistically (<90% CI) overperformed and 20.5% underperformed. An interactive tool based on the OCT analysis automatically reveals 14 hospital-specific patient cohorts, simultaneously assessing overperformance or underperformance, and enabling further analysis of cohort strata in any chosen time frame. ConclusionsMachine learning-based OCT benchmarking analysis provides automatic assessment of hospital-specific case-adjusted performance after congenital heart surgery, not only overall but importantly, also by similar risk patient cohorts. This is a tool for hospital self-assessment, particularly facilitated by the user-accessible online-platform.

Predictors of hospital mortality in older adults with and without dementia: a necessary step towards personalized prognostication

AbstractBackgroundDementia has been associated with increased risk of hospital mortality (1). However, predictors of hospital mortality for patients with dementia have not been widely addressed. We aimed to investigate predictors of hospital mortality according to preexisting clinical dementia in older adults.MethodsWe analyzed data from a prospective cohort study in Sao Paulo, Brazil. We included acutely ill patients aged ≥60 years admitted to a tertiary university hospital between 2009 and 2020. Candidates underwent comprehensive geriatric assessments in the first 24 hours of hospitalization. Preexisting clinical dementia (after this, referred to as dementia) was determined by a Clinical Dementia Rating (CDR) ≥1 (2). We investigated independent predictors of hospital mortality according to dementia using multivariable Poisson regressions. We further analyzed our multivariable models' accuracy in predicting hospital mortality using area under the ROC curves (AUC).ResultsWe included 3,863 participants (mean age = 80; female = 62%). Overall, 1,761 participants (46%) had dementia. Mortality was 11% in participants without dementia and 21% in those with dementia (p&lt;0.001). In participants without dementia, mortality was independently predicted by age, multimorbidity, functional status, delirium, nutritional status, and serum albumin (Table 1). However, in participants with dementia, mortality was also predicted by illness acuity and creatinine. Finally, we observed that our multivariable model had an AUC = 0.88 (95%CI = 0.86‐0.90) in participants without dementia and 0.80 (0.78‐0.83) in those with dementia (Figure 1).ConclusionOlder adults with dementia were twice as likely to die in the hospital than those without dementia. Moreover, independent predictors of hospital mortality were not necessarily shared between patients with and without dementia. Given the importance of prognostic assessments for decision‐making processes, researchers should consider exploring new algorithms that differ according to baseline cognition.

A Mortality Prediction Score for Patients With Veno-Venous Extracorporeal Membrane Oxygenation (VV-ECMO): The PREDICT VV-ECMO Score.

Mortality prediction for patients with the severe acute respiratory distress syndrome (ARDS) supported with veno-venous extracorporeal membrane oxygenation (VV-ECMO) is challenging. Clinical variables at baseline and on day 3 after initiation of ECMO support of all patients treated from October 2010 through April 2020 were analyzed. Multivariate logistic regression analysis was used to identify score variables. Internal and external (Monza, Italy) validation was used to evaluate the predictive value of the model. Overall, 272 patients could be included for data analysis and creation of the PREDICT VV-ECMO score. The score comprises five parameters (age, lung fibrosis, immunosuppression, cumulative fluid balance, and ECMO sweep gas flow on day 3). Higher score values are associated with a higher probability of hospital death. The score showed favorable results in derivation and external validation cohorts (area under the receiver operating curve, AUC derivation cohort 0.76 [95% confidence interval, CI, 0.71-0.82] and AUC validation cohort 0.74 [95% CI, 0.67-0.82]). Four risk classes were defined: I ≤ 30, II 31-60, III 61-90, and IV ≥ 91 with a predicted mortality of 28.2%, 56.2%, 84.8%, and 96.1%, respectively. The PREDICT VV-ECMO score suggests favorable performance in predicting hospital mortality under ongoing ECMO support providing a sound basis for further evaluation in larger cohorts.

Critically ill patients with high predicted mortality: Incidence and outcome

ObjectiveAs calculated by the severity scores, an unknown number of patients are admitted to the Intensive Care Unit (ICU) with a very high risk of death. Clinical studies have poorly addressed this population, and their prognosis is largely unknown. DesignPost hoc analysis of a multicenter, cohort, longitudinal, observational, retrospective study (CIMbA). SettingSixteen Portuguese multipurpose ICUs. PatientsPatients with a Simplified Acute Physiology Score II (SAPS II) predicted hospital mortality above 80% on admission to the ICU (high-risk group); A comparison with the remaining patients was obtained. InterventionsNone. Main Variables of InterestHospital, 30 days, 1 year mortality. ResultsWe identified 4546 patients (59.9% male), 12.2% of the whole population. Their SAPS II predicted hospital mortality was 89.0±5.8%, whilst the observed mortality was lower, 61.0%. This group had higher mortality, both during the first 30 days (aHR 3.52 [95% CI 3.34–3.71]) and from day 31 to day 365 after ICU admission (aHR 1.14 [95%CI 1.04–1.26]), respectively. However, their hospital standardized mortality ratio was similar to the other patients (0.69 vs. 0.69, P=.92). At one year of follow-up, 30% of patients in the high-risk group were alive. ConclusionsRoughly 12% of patients admitted to the ICU for more than 24h had a SAPS II score predicted mortality above 80%. Their hospital standardized mortality was similar to the less severe population and 30% were alive after one year of follow-up.

Predictors of hospital mortality and multidrug-resistant pathogens in hospitalized pneumonia patients residing in the community

Background and objectiveThe 2019 ATS/ADSA guidelines for adult community-acquired pneumonia (CAP) eliminated healthcare-associated pneumonia (HCAP) and considered it to be a form of CAP. This concept, however, was based on studies with relatively small sample sizes. MethodsWe investigated the risk factors of 30-day mortality, and methicillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa infections in patients with pneumonia coming from the community using the Diagnosis Procedure Combination database, a nationwide discharge database of acute care hospitals. Furthermore, we compared these factors between CAP and HCAP. ResultsA total of 272,337 patients aged ≥20 years with pneumonia were grouped into 145,082 CAP patients and 127,255 HCAP patients. The 30-day mortality rate (8.9 % vs.3.3 %), MRSA infection (2.4 % vs. 1.4 %), and Pseudomonas aeruginosa infection (1.6 % vs. 1.0 %) were significantly higher in HCAP than in CAP patients. Multivariable logistic regression analysis showed that 12 of 13 identified predictors of mortality (i.e., high age, male, underweight, non-ambulatory status, bedsore, dehydration, respiratory failure, consciousness disturbance, hypotension, admitted in critical care, comorbidity of heart failure, and chronic obstructive pulmonary disease) were identical in CAP and HCAP patients. Similarly, five of six distinct risk factors for MRSA infection, and three of three for Pseudomonas aeruginosa infection were identical between the patients. ConclusionThe risk factors for mortality and MRSA or Pseudomonas aeruginosa infection were almost identical in patients with CAP and HCAP. The assessment of individual risk factors for mortality and MRSA or Pseudomonas aeruginosa infection in CAP and abandoning categorization as HCAP can improve and simplify empiric therapy.

Primary or Delayed Repair for Complete Atrioventricular Septal Defect, Tetralogy of Fallot, and Ventricular Septal Defect: Relationship to Country Economic Status.

Primary repair in the first six months of life is routine for tetralogy of Fallot, complete atrioventricular septal defect, and ventricular septal defect in high-income countries. The objective of this analysis was to understand the utilization and outcomes of palliative and reparative procedures in high versus middle-income countries. The World Database of Pediatric and Congenital Heart Surgery identified patients who underwent surgery for: tetralogy of Fallot, complete atrioventricular septal defect, and ventricular septal defect. Patients were categorized as undergoing primary repair, repair after prior palliation, or palliation only. Country economic status was categorized as lower middle, upper middle, and high, defined by the World Bank. Multiple logistic regression models were utilized to identify independent predictors of hospital mortality. Economic categories included high (n = 571, 5.3%), upper middle (n = 5,342, 50%), and lower middle (n = 4,793, 49.7%). The proportion of patients and median age with primary repair were: tetralogy of Fallot, 88.6%, 17.7 months; complete atrioventricular septal defect, 83.4%, 7.7 months; and ventricular septal defect, 97.1%, ten months. Age at repair was younger in high income countries (P < .0001). Overall mortality after repair was lowest in high income countries. Risk factors for hospital mortality included prematurity, genetic syndromes, and urgent or emergent operations (all P < .05). Primary repair was selected in >90% of patients, but definitive repair was delayed in lower and upper middle income countries compared with high-income countries. Repair after prior palliation versus primary repair was not a risk factor for hospital mortality. Initial palliation continues to have a small but important role in the management of these three specific congenital heart defects.

Sex-Based Differences in Early Outcomes Following Mitral Valve Surgery for Degenerative Disease

Objectives: To determine whether sex-based differences exist following surgery for degenerative mitral valve disease. Methods: Using a national database, we analysed data on mitral valve surgery for degenerative disease (n = 22,658) between January 2000 and March 2019 in the UK. We split the cohort into men (n = 14,681) and women (n = 7977) and compared background characteristics, intraoperative variables and early postoperative outcomes. Our primary outcome was hospital mortality; secondary outcomes included re-exploration for bleeding, prolonged admission (&gt;10 days) and mitral replacement. We used binary logistic regression models for all outcomes, with multiplicative interaction terms to determine the nature of any differences. Results: Women presented older (70 ± 11 years vs. 67 ± 11 years, p &lt; 0.001) with worse symptom profiles (New York Heart Association Class III–IV 57% vs. 44%, p &lt; 0.001). They had higher rates of preoperative atrial fibrillation (39% vs. 35%, p &lt; 0.001) and tricuspid disease requiring surgery (21% vs. 15%, p &lt; 0.001). They had lower repair rates (66% vs. 76%, p &lt; 0.001), higher mortality (3% vs. 2%, p &lt; 0.001) and were more likely to have a prolonged admission (48% vs. 40%, p &lt; 0.001). Female sex was an independent predictor of mortality (odds ratio (OR): 1.52, 95% CI: 1.21–1.90, p &lt; 0.001). Age and Canadian Cardiovascular Society (CCS) score showed significant interactions with sex. The relationship between advancing age and mortality was found to be more pronounced in women. Conclusions: (1) Female sex is an independent predictor of hospital mortality, prolonged hospital admission and mitral valve replacement. (2) The relationship between female sex and mortality is exacerbated by worsening CCS score and advancing age. (3) Women have significantly lower repair rates.