Prolonged Hospital Length Of Stay Research Articles

Association Between Congenital Heart Disease-Related Diagnosis Codes and Trauma Surgery Outcomes.

Congenital heart disease (CHD) is one the most common congenital anomalies, with a prevalence of 8-10 cases per 1000 live births in the United States. Congenital heart disease has been recognized as a risk factor for poor perioperative and postoperative outcomes in non-cardiac surgery. We aimed to determine if documentation of CHD-related diagnosis codes was associated with similar risks for trauma surgery. Data were acquired from the 2010-2019 American College of Surgeons' Trauma Quality Programs Participant Use Files. This study included trauma patients of all ages with one or more surgical procedures and at least one documented non-trauma (comorbidity) International Classification of Diseases code. Patients were stratified based on presence of CHD-related comorbidity codes vs any other comorbidity. Outcomes included mortality, hospital length of stay (LOS), discharge disposition, and in-hospital complications. Using 1:1 propensity score matching, we matched 215 cases with CHD-related comorbid diagnoses to non-CHD controls. Compared to patients with other comorbidities, patients with CHD-related comorbidites were less likely to be discharged home to self-care (odds ratio: 0.44, 95% confidence interval [CI]: 0.25, 078 P = .005) and tended to have prolonged hospital LOS (incidence rate ratio [IRR]: 1.06, 95% CI: 1.001, 1.13, P = .046). We present the first quantitative multicenter analysis correlating documentation of comorbid CHD-related diagnoses with higher risk of adverse outcomes after trauma surgery. These results support the need to routinely acknowledge and document CHD as comorbidity in trauma admissions that could lead to surgical intervention and for trauma centers to prepare for patients with a possible CHD comorbidity.

Random forest predictive modeling of prolonged hospital length of stay in elderly hip fracture patients

BackgroundIn elderly individuals suffering from hip fractures, a prolonged hospital length of stay (PLOS) not only heightens the probability of patient complications but also amplifies mortality risks. Yet, most elderly hip fracture patients present compromised baseline health conditions. Additionally, PLOS leads to increased expenses for patient treatment and care, while also diminishing hospital turnover rates. This, in turn, jeopardizes the prompt allocation of beds for urgent cases.MethodsA retrospective study was carried out from October 2021 to November 2023 on 360 elderly hip fracture patients who underwent surgical treatment at West China Hospital. The 75th percentile of the total patient cohort’s hospital stay duration, which was 12 days, was used to define prolonged hospital length of stay (PLOS). The cohort was divided into training and testing datasets with a 70:30 split. A predictive model was developed using the random forest algorithm, and its performance was validated and compared with the Lasso regression model.ResultsOut of 360 patients, 103 (28.61%) experienced PLOS. A Random Forest classification model was developed using the training dataset, identifying 10 essential variables. The Random Forest model achieved perfect performance in the training set, with an area under the curve (AUC), balanced accuracy, Kappa value, and F1 score of 1.000. In the testing set, the model’s performance was assessed with an AUC of 0.846, balanced accuracy of 0.7294, Kappa value of 0.4325, and F1 score of 0.6061.ConclusionThis study aims to develop a prognostic model for predicting delayed discharge in elderly patients with hip fractures, thereby improving the accuracy of predicting PLOS in this population. By utilizing machine learning models, clinicians can optimize the allocation of medical resources and devise effective rehabilitation strategies for geriatric hip fracture patients. Additionally, this method can potentially improve hospital bed turnover rates, providing latent benefits for the healthcare system.

IMPROVE THE EFFECTIVENESS OF PREDICTIVE MODELS FOR LENGTH OF HOSPITAL STAY (LOS) BY ADDING ELECTRONIC HEALTH RECORDS DATA: A RETROSPECTIVE STUDY IN PATIENTS WITH HEART FAILURE

Abstract Background Length of hospital stay (LOS) is considered a key process that has a clinical, economic, and organizational impact representative of the quality of care. In particular, in patients with heart failure (HF), which is a life–threatening multifactorial syndrome and a major public health problem, when a prolonged LOS (PLOS) occurs, frequent readmission and high mortality are common, with worrying consequences for health systems. Although the LOS is often explained by standard medical data (e.g., comorbidities), it may depend on many other patient characteristics. Therefore, considering additional elements collected in electronic health records (EHRs) could be crucial to predict the occurrence of a LOS and correctly estimating this risk. The aim of this study is to demonstrate the predictive power of clinical and organizational data collected through an EHR on LOS in HF patients. Methods A descriptive, retrospective study was conducted in an Italian university hospital. Data related to HF inpatients consecutively admitted across 1 year were collected from the hospital’s HER. PLOS was defined as a stay ≥ 14 days. Sociodemographic data, administrative data, and clinical data (such as the number of active medical problems, the number of medications, frailty, and nursing dependency), were tested in a predictive model as potential determinants of LOS. Results A total of 608 HF patients were included. The mean age of patients was 76 ± 13.06 years (range: 23–102); 61.5% were male. Prolonged LOS was found in 25.2% of patients. The univariable linear regression model explained 10.2% of LOS variance. Being female, living without social support, having an increased number of previous admissions and nursing dependency, being dependent on functional status, having a worse behavior pattern and mobility, and experiencing a sensory deficit were significant determinants (p < .05) of LOS. Conclusion The inclusion of EHRs data in addition to clinical variables, can help clinicians and researchers to improve the predictive ability on LOS, ensuring its early identification and ultimately enhancing the patients’ health outcomes through the implementation of targeted interventions.

The PAncreatic Surgery Composite Endpoint PACE – Development and Validation of a Clinically Relevant Endpoint Requiring Lower Sample Sizes

Abstract Background Single endpoints in prospective and randomized studies have become impractical due to their low frequency and the marginal benefit of new interventions. Aims To provide a composite endpoint in pancreatic surgery. Methods Data from prospective studies were used to develop (n=1273) and validate (n=544) a composite endpoint based on postoperative pancreatic fistula, post-pancreatectomy hemorrhage as well as reoperation and reinterventions. All patients had pancreatectomies of different extents. The association of the developed PAncreatic surgery Composite Endpoint (PACE) with prolonged length of hospital stay (LOS) >75th percentile and mortality were assessed. A single-institution database was used for external validation (n = 2666). Sample size calculations were made for single outcomes and the composite endpoint. Results In the internal validation cohort, the PACE demonstrated an AUC of 78.0%, a sensitivity of 90.4% and a specificity of 67.6% in predicting a prolonged LOS. In the external cohort, the AUC was 76.9%, the sensitivity 73.8% and the specificity 80.1%. The 90-day mortality rate was significantly different for patients with a positive versus a negative PACE both in the development and internal validation cohort (5.1% vs 0.9%; P< 0.001), as well as in the external validation cohort (8.5% vs 1.2%, P< 0.001). The PACE enabled sample size reductions of up to 80.5% compared to single outcomes. Conclusion The PACE performed well in predicting prolonged hospital stays and can be used as a standardized and clinically relevant endpoint for future prospective trials enabling lower sample sizes and therefore improved feasibility compared to single outcome parameters.

A Prediction Model for Prolonged Hospital Length of Stay (ProLOS) in Coronavirus Disease 2019 (COVID-19) Patients.

Coronavirus disease 2019 (COVID-19) is an acute respiratory infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With the normalization of COVID-19 globally, it is crucial to construct a prediction model that enables clinicians to identify patients at risk for ProLOS based on demographics and serum inflammatory biomarkers. The study included hospitalized patients with a confirmed diagnosis of COVID-19. These patients were randomly grouped into a training (80%) and a test (20%) cohort. The LASSO regression and ten-fold cross-validation method were applied to filter variables. The training cohort utilized multifactorial logistic regression analyses to identify the independent factors of ProLOS in COVID-19 patients. A 4-variable nomogram was created for clinical use. ROC curves were plotted, and the area under the curve (AUC) was calculated to evaluate the model's discrimination; calibration analysis was planned to assess the validity of the nomogram, and decision curve analysis (DCA) was used to evaluate the clinical usefulness of the model. The results showed that among 310 patients with COVID-19, 80 had extended hospitalization (80/310). Four independent risk factors for COVID-19 patients were identified: age, coexisting chronic respiratory diseases, white blood cell count (WBC), and serum albumin (ALB). A nomogram based on these variables was created. The AUC in the training cohort was 0.808 (95% CI: 0.75 - 0.8671), and the AUC in the test cohort was 0.815 (95% CI: 0.7031 - 0.9282). The model demonstrates good calibration and can be used with threshold probabilities ranging from 0% to 100% to obtain clinical net benefits. A predictive model has been created to accurately predict whether the hospitalization duration of COVID-19 patients will be prolonged. This model incorporates serum WBC, ALB levels, age, and the presence of chronic respiratory system diseases.

Predicting Prolonged Length of Hospital Stay and Identifying Risk Factors Following Total Ankle Arthroplasty: A Supervised Machine Learning Methodology

Ankle osteoarthritis (OA) is a debilitating condition that arises as a result of trauma or injury to the ankle and often progresses to chronic pain and loss of function that may require surgical intervention. Total ankle arthroplasty (TAA) has emerged as a means of operative treatment for end-stage ankle OA. Increased hospital length of stay (LOS) is a common adverse postoperative outcome that increases both the complications and cost of care associated with arthroplasty procedures. The purpose of this study was to employ four machine learning (ML) algorithms to predict LOS in patients undergoing TAA using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. The ACS-NSQIP database was queried to identify adult patients undergoing elective TAA from 2008 to 2018. Four supervised ML classification algorithms were utilized and tasked with predicting increased hospital length of stay (LOS). Among these variables, female sex, ASA Class III, preoperative sodium, preoperative hematocrit, diabetes, preoperative creatinine, other arthritis, BMI, preoperative WBC, and Hispanic ethnicity carried the highest importance across predictions generated by 4 independent ML algorithms. Predictions generated by these algorithms were made with an average AUC of 0.7257, as well as an average accuracy of 73.98% and an average sensitivity and specificity of 48.47% and 79.38%, respectively. These findings may be useful for guiding decision-making within the perioperative period and may serve to identify patients at increased risk for a prolonged LOS.

Related factors associated with the prognosis of children undergoing liver transplantation under the enhanced recovery after surgery nursing concept.

This study aimed to investigate factors associated with the clinical outcomes of patients who underwent pediatric liver transplantation (LT) and received enhanced recovery after surgery (ERAS) nursing. A cohort of 104 pediatric patients was studied at our hospital. Data on 8 indicators and 2 clinical outcomes, including length of hospital stay (LOS) and 30-day readmission rates, were collected. Linear and logistic regression analyses were employed to examine the associations of the 8 indicators with hospital-LOS and readmission risks, respectively. The predictive value of these indicators for the outcomes was determined using the receiver operating characteristic (ROC) curve, decision curve analysis, and importance ranking through the XGBoost method. A comprehensive model was developed to evaluate its predictive accuracy. Regression analyses identified donor age, donor gender, and intensive care unit (ICU)-LOS of recipients as significant predictors of hospital LOS (all P < .05), whereas no indicators were significantly associated with readmission risk. Further, ROC analysis revealed that 3 indicators provided superior prediction for 28-day hospital LOS compared to the median LOS of 18 days. ICU-LOS demonstrated the highest clinical net benefit for predicting 28-day hospital-LOS. Multivariable regression analysis confirmed the independent predictive value of donor age and ICU-LOS for the hospital-LOS (all β > 0, all P < .05). Although the comprehensive model incorporating donor age and ICU-LOS showed stable predictive capability for hospital-LOS, its performance did not significantly exceed that of the individual indicators. In pediatric LT, hospital LOS warrants greater emphasis over readmission rates. Donor age and ICU-LOS emerged as independent risk factors associated with prolonged hospital LOS.

The Predictive and Prognostic Value of Percentage Change in Calf Circumference in Infants and Children During the First Week of Admission in the Pediatric Intensive Care Unit: A Prospective Cohort Study.

Calf circumference (CC) is an easy measure of muscle loss; yet, there are limited studies in children. We aimed to assess the viability of the first-week percent change in CC as a prognostic nutritional marker. Two MUAC (mid-upper arm circumference), CC, and weight measurements were carried out: one on the first day of admission and another on the seventh day. The differences and correlations between their percent changes were examined. The possible association between CC percent change and prolonged length of hospital stay (PLOS) and in-hospital mortality was assessed. Seventy participants were enrolled with a median age of 19.5 months. The median (interquartile range [IQR]) of CC percent change was 0.54 (5.5) with significant correlations between it and each of MUAC and weight percent change (ρ = 0.928, P = .001), (ρ = 0.902, P = .001), respectively. Calf circumference percent changes were significantly more negative in children with PLOS; (P = .004). These changes seem to be viable measures of nutritional status.

Economic burden attributable to hospital-acquired infections among tumor patients from a large regional cancer center in Southern China

To evaluate the economic loss of hospital-acquired infections (HAIs) among tumor patients so as to help policymakers to allocate health care resources and address the issue. We conducted a retrospective, 1:1 matched case-control study in a large region cancer hospital between January 1 and December 31, 2022. The economic burden was estimated as the median of the 1:1 pair differences of various hospitalization fees and hospital length of stay (LOS). In this study of 329 matched pairs, the patients with HAIs incurred higher hospitalization cost (ie, $16,927) and experienced longer hospital LOS (ie, 22 days), compared to the non-HAI groups. The extra hospitalization cost and the prolonged hospital LOS caused by HAIs were $4,919 and 9 days, respectively. Accordingly, the direct nonmedical economic loss attributable to HAI was approximately $478 to 835 per case. Furthermore, the increment of hospitalization costs varied by sites of infection, types of tumors, and stratum of age. HAIs lead to the increment of direct economic burden and hospital LOS in tumor patients. Our findings highlight the importance of implementing effective infection control measures in hospitals to reduce the financial burden on tumor patients.

Medicaid Insurance is a Predictor of Prolonged Hospital Length of Stay After Traumatic Brain Injury: A Stratified National Trauma Data Bank Cohort Analysis of 552 949 Patients.

Hospital length of stay (HLOS) is a metric of injury severity, resource utilization, and healthcare access. Recent evidence has shown an association between Medicaid insurance and increased HLOS after traumatic brain injury (TBI). This study aims to validate the association between Medicaid and prolonged HLOS after TBI using the National Trauma Data Bank. National Trauma Data Bank Trauma Quality Programs Participant Use Files (2003-2021) were queried for adult patients with TBI using traumatic intracranial injury ICD-9/ICD-10 codes. Patients with complete HLOS, age, sex, race, insurance payor, Glasgow Coma Scale, Injury Severity Score, and discharge disposition data were included (N = 552 949). Analyses were stratified by TBI severity using Glasgow Coma Scale. HLOS was coded into Tiers according to percentiles within TBI severity categories (Tier 1: 1-74th; 2: 75-84th; 3: 85-94th; 4: 95-99th). Multivariable logistic regressions evaluated associations between insurance payor and prolonged (Tier 4) HLOS, controlling for sociodemographic, Injury Severity Score, cranial surgery, and discharge disposition variables. Adjusted odds ratios (aOR) and 95% CI were reported. HLOS Tiers consisted of 0-19, 20-27, 28-46, and ≥47 days (Tiers 1-4, respectively) in severe TBI (N = 103 081); 0-15, 16-21, 22-37, and ≥38 days in moderate TBI (N = 39 904); and 0-7, 8-10, 11-19, and ≥20 days in mild TBI (N = 409 964). Proportion of Medicaid patients increased with Tier ([Tier 1 vs Tier 4] severe: 16.0% vs 36.1%; moderate: 14.1% vs 31.6%; mild TBI: 10.2% vs 17.4%; all P < .001). On multivariable analyses, Medicaid was associated with prolonged HLOS (severe TBI: aOR = 2.35 [2.19-2.52]; moderate TBI: aOR = 2.30 [2.04-2.61]; mild TBI: aOR = 1.75 [1.67-1.83]; reference category: private/commercial). This study supports Medicaid as an independent predictor of prolonged HLOS across TBI severity strata. Reasons may include different efficacies in care delivery and reimbursement, which require further investigation. Our findings support the development of discharge coordination pathways and policies for Medicaid patients with TBI.

Differences in the association between epidural analgesia and length of stay by surgery type: an observational study

IntroductionDespite a decline in the use of thoracic epidural analgesia related in part to concerns for delayed discharge, it is unknown whether changes in length of stay (LOS) associated with...

(P82) Predictors of Prolonged Hospital Length of Stay After Traumatic Brain Injury

(P82) Predictors of Prolonged Hospital Length of Stay After Traumatic Brain Injury

The PAncreatic Surgery Composite Endpoint PACE - Development and Validation of a Clinically Relevant Endpoint Requiring Lower Sample Sizes.

To provide a composite endpoint in pancreatic surgery. Single endpoints in prospective and randomized studies have become impractical due to their low frequency and the marginal benefit of new interventions. Data from prospective studies were used to develop (n=1273) and validate (n=544) a composite endpoint based on postoperative pancreatic fistula, post-pancreatectomy hemorrhage as well as reoperation and reinterventions. All patients had pancreatectomies of different extents. The association of the developed PAncreatic surgery Composite Endpoint (PACE) with prolonged length of hospital stay (LOS) >75th percentile and mortality was assessed. A single-institution database was used for external validation (n = 2666). Sample size calculations were made for single outcomes and the composite endpoint. In the internal validation cohort, the PACE demonstrated an AUC of 78.0%, a sensitivity of 90.4% and a specificity of 67.6% in predicting a prolonged LOS. In the external cohort, the AUC was 76.9%, the sensitivity 73.8% and the specificity 80.1%. The 90-day mortality rate was significantly different for patients with a positive versus a negative PACE both in the development and internal validation cohort (5.1% vs 0.9%; P< 0.001), as well as in the external validation cohort (8.5% vs 1.2%, P< 0.001). The PACE enabled sample size reductions of up to 80.5% compared to single outcomes. The PACE performed well in predicting prolonged hospital stays and can be used as a standardized and clinically relevant endpoint for future prospective trials enabling lower sample sizes and therefore improved feasibility compared to single outcome parameters.

Novel Preoperative Risk Stratification Using Digital Phenotyping Applying a Scalable Machine-Learning Approach.

Classification of perioperative risk is important for patient care, resource allocation, and guiding shared decision-making. Using discriminative features from the electronic health record (EHR), machine-learning algorithms can create digital phenotypes among heterogenous populations, representing distinct patient subpopulations grouped by shared characteristics, from which we can personalize care, anticipate clinical care trajectories, and explore therapies. We hypothesized that digital phenotypes in preoperative settings are associated with postoperative adverse events including in-hospital and 30-day mortality, 30-day surgical redo, intensive care unit (ICU) admission, and hospital length of stay (LOS). We identified all laminectomies, colectomies, and thoracic surgeries performed over a 9-year period from a large hospital system. Seventy-seven readily extractable preoperative features were first selected from clinical consensus, including demographics, medical history, and lab results. Three surgery-specific datasets were built and split into derivation and validation cohorts using chronological occurrence. Consensus k -means clustering was performed independently on each derivation cohort, from which phenotypes' characteristics were explored. Cluster assignments were used to train a random forest model to assign patient phenotypes in validation cohorts. We reconducted descriptive analyses on validation cohorts to confirm the similarity of patient characteristics with derivation cohorts, and quantified the association of each phenotype with postoperative adverse events by using the area under receiver operating characteristic curve (AUROC). We compared our approach to American Society of Anesthesiologists (ASA) alone and investigated a combination of our phenotypes with the ASA score. A total of 7251 patients met inclusion criteria, of which 2770 were held out in a validation dataset based on chronological occurrence. Using segmentation metrics and clinical consensus, 3 distinct phenotypes were created for each surgery. The main features used for segmentation included urgency of the procedure, preoperative LOS, age, and comorbidities. The most relevant characteristics varied for each of the 3 surgeries. Low-risk phenotype alpha was the most common (2039 of 2770, 74%), while high-risk phenotype gamma was the rarest (302 of 2770, 11%). Adverse outcomes progressively increased from phenotypes alpha to gamma, including 30-day mortality (0.3%, 2.1%, and 6.0%, respectively), in-hospital mortality (0.2%, 2.3%, and 7.3%), and prolonged hospital LOS (3.4%, 22.1%, and 25.8%). When combined with the ASA score, digital phenotypes achieved higher AUROC than the ASA score alone (hospital mortality: 0.91 vs 0.84; prolonged hospitalization: 0.80 vs 0.71). For 3 frequently performed surgeries, we identified 3 digital phenotypes. The typical profiles of each phenotype were described and could be used to anticipate adverse postoperative events.

Outcomes Among Hospitalized Patients with Acute Chest Syndrome and Concomitant Clinical Depression: A United States Population-Based Cohort Study

Background Acute chest syndrome is a life-threatening complication of sickle cell disease, seen in both children and adults. Hospitalization and immediate treatment are necessary to prevent morbidity and mortality. Clinical depression is quite prevalent in patients with sickle cell disease, and review of literature reveals association of depression with increased annual emergency department visits, hospitalizations, and blood transfusions in patients with sickle cell disease. Whether this leads to worse outcomes is yet to be ascertained. Methods We utilized the 2018-2020 National Inpatient Sample (NIS) Database in conducting a retrospective cohort study. We identified hospitalized patients with acute chest syndrome and concomitant clinical depression using appropriate ICD-10 CM codes. We stratified patients based on whether they had clinical depression during the hospitalization or not. A survey multivariable logistic and linear regression analysis was used to calculate adjusted odds ratios (OR) for the primary and secondary outcomes. A p value of &amp;lt;0.05 was considered statistically significant. The aim of this study is to investigate the outcomes among hospitalized patients with acute chest syndrome and comorbid clinical depression looking primarily at the in-hospital mortality and hospital length of stay (LOS). Results We identified a total of 5271 hospitalized patients with acute chest syndrome, of which 10.72% (565/5271) had comorbid clinical depression. The overall in-hospital mortality among those with acute chest syndrome was 3.70% (195/5271). Among those with clinical depression, the overall in-hospital mortality rate was not significantly higher at 5.31% (30/565) (p=0.385). Utilizing a stepwise survey multivariable logistic regression model that adjusted for patient and hospital level confounders, clinical depression among hospitalized patients with acute chest syndrome did not significantly increase the risk for in-hospital mortality (adjusted OR 1.39, 95% confidence interval [CI], 0.49-3.91; p=0.533) or prolong hospital LOS (median LOS 6 days versus 7 days, p=0.631) Conclusion Acute chest syndrome continues to be an important cause of hospital admissions in patients with sickle cell disease. Our analysis revealed that clinical depression is commonly encountered in this subset of patients. Clinical depression did not have a statistically significant impact on outcomes including increased in-hospital mortality or prolonged hospital LOS. Prospective studies with large sample sizes are needed to better understand these outcomes.

Intravenous fluid therapy and hospital outcomes for vaso-occlusive episodes in children, adolescents, and young adults with sickle cell disease.

While intravenous fluid (IVF) therapy in patients with sickle cell disease (SCD) admitted for a vaso-occlusive episode (VOE) can help reduce red blood cell sickling, clinical practice varies across institutions. We examined the relationship between IVF therapy and hospital length of stay (HLOS), as well as adverse events, such as acute chest syndrome (ACS), pediatric intensive care unit (PICU) transfer, and 28-day re-admission. This is a single-center retrospective analysis of SCD VOE hospitalizations between January 2015 and April 2020. Patients with SCD, age 0-30, with consecutive hospitalizations for VOE were included. For the first 3days of each admission, an "IVF ratio" was calculated by dividing actual IVF rate administered by weight-based maintenance IVF (mIVF) rate. A total of 617 hospitalizations for 161 patients were included. Mean HLOS was 5.7days, (SD 3.9), and mean IVF volume over the first 3days of admission was 139.6mL/kg/day (SD 57.8). Multivariate analysis showed that for each additional 0.5 times the mIVF rate, HLOS increased by 0.53day (p<.001; 95% confidence interval [CI]: 0.609-0.989), but there was no significant association between IVF therapy and adverse events. History of chronic pain was associated with increased odds of re-admission (OR 6.4; 95% CI: 3.93-10.52). Despite the theoretical potential for IVF therapy to slow down the sickling process, our findings suggest that increased IVF therapy was associated with prolonged HLOS, which places a burden on patients, families, and the health system.

Exploring the impact of left ventricular diastolic dysfunction on postoperative cardiac surgery outcomes, with a focus on sex disparities: a comprehensive literature review

IntroductionLeft ventricular diastolic dysfunction (LVDD) is known to cause worse outcomes after cardiac surgery. Females have twice the risk of developing LVDD compared with males. The aim of this literature review was to evaluate the association between LVDD and early (≤30 days) outcomes after cardiac surgery, including sex disparities.MethodsWe identified 454 studies in Medline and Embase, of which 19 were included. Articles were assessed for inclusion of female patients with LVDD, ≥50 years of age undergoing cardiac surgery (on or off-pump) or transcatheter aortic valve replacement. The primary outcome was early postoperative mortality. Secondary outcomes were early postoperative complications, ICU length of stay (ICULOS), hospital length of stay (HLOS), and other sex-related postoperative outcomes.ResultsIn patients with LVDD, the majority of studies showed that higher LVDD grades correlate with higher early postoperative mortality. In patients with LVDD, who underwent on-pump cardiac surgery, female sex was significantly associated with higher LVDD grades and baseline E/e' ratios. Females with LVDD, were associated with prolonged HLOS after off-pump coronary artery bypass grafting (CABG). In combined cardiac surgery, a twofold increased risk of prolonged HLOS and increased ICULOS was reported. Furthermore, increased left ventricular end-diastolic filling pressure, an increased need for postoperative inotropic support, and difficult separation from cardiopulmonary bypass were observed.DiscussionDespite the limited number of studies focusing on sex differences, females with LVDD appear to have worse early outcomes after cardiac surgery compared to men with LVDD. Future research will need to identify sex-specific risk factors and target treatment optimization.

Predictors of length of hospital stay for preterm infants in Ethiopia: a competing risk analysis.

Length of hospital stay (LOS) is one of the essential indicators for evaluating the efficiency and the quality-of-care service delivered. predicting LOS is critical for resource allocation, decision-making, lowering neonatal morbidity and death, enhancing clinical outcomes and parent counseling. In addition, extended hospital stays (long LOS_NICU) place a burden on the healthcare systems decreasing bed turnover rates as well as their financial stand and the mental stress on families. In Ethiopia, there is limited evidence on the determinant factors that influence on LOS. To determine factors affecting neonatal intensive care unit length of stay for all preterm newborns who were discharged alive. The study used a secondary data source, was collected for the Study of Illness in Preterm (SIP) infants project. The research study was a multicenter, cross-sectional, observational clinical study that took place in five Ethiopia hospitals from July 1, 2016, to May 31, 2018. The predictors of LOS were determined using Fine-Gray's competing risk analysis. For this study 3,511 preterm infants admitted to the NICU were analyzed. About 28.8% of the preterm infants died during their time in neonatal care while 66.6% were discharged alive. At the end of the study 4.6% babies were still in the NICU. The overall median LOS (death or discharge) was 7 days, with an interquartile range of 8 days. The cumulative incidence of discharge rose with increasing in gestational age and birth weight, on the contrary, the rate of discharge was decreased by 45.7% with the development of RDS (SDH ratio: 0.543), by 75.9% with the development of apnea (SDH ratio: 0.241), by 36.2% with sepsis, and by 43.6% with pneumonia (SDH ratio: 0.564). Preterm newborns with a low gestational age and birth weight have a greater probability of having a prolonged LOS. Complications of the medical conditions RDS, apnea, sepsis, pneumonia, anemia, asphyxia, and NEC substantially raise LOS considerably.

Impact of changes in skeletal muscle mass and quality during the waiting time on outcomes of lung transplantation.

The association of changes in skeletal muscle mass and quality during the waiting time with outcomes of lung transplantation (LT) remains unclear. We aimed to examine the association of changes in skeletal muscle mass and quality during the waiting time, as well as preoperative skeletal muscle mass and quality, with outcomes of LT. This study included individuals who underwent LT from brain-dead donors. Skeletal muscle mass (cm2 /m2 ) and quality (mean Hounsfield units [HU]) of the erector spinae muscle at the 12th thoracic level were evaluated using computed tomography. Preoperative skeletal muscle mass and quality, and their changes during the waiting time were calculated. We evaluated the associations among mechanical ventilation (MV) duration, intensive care unit (ICU) length of stay (LOS), hospital LOS, 6-minute walk distance at discharge, and 5-year survival after LT. This study included 98 patients. The median waiting time was 594.5days (interquartile range [IQR], 355.0-913.0). The median changes in skeletal muscle mass and quality were -4.4% (IQR, -13.3-3.1) and -2.9% (IQR, -16.0-4.1), respectively. Severe low skeletal muscle mass at LT was associated with prolonged ICU LOS (B=8.46, 95% confidence interval [CI]: .51-16.42) and hospital LOS (B=36.00, 95% CI: 3.23-68.78). Pronounced decrease in skeletal muscle mass during the waiting time was associated with prolonged MV duration (B=7.85, 95% CI: .89-14.81) and ICU LOS (B=7.97, 95% CI: .83-15.10). Maintaining or increasing skeletal muscle mass during the waiting time would be beneficial to improve the short-term outcomes of LT.

Machine Learning-Based Prediction of Complications and Prolonged Hospitalization with the GLIM Criteria Combinations Containing Calf Circumference in Elderly Asian Patients.

Malnutrition is widely present and influences the prognosis of elderly inpatients, so it is helpful to be able to identify it with a convenient method. However, in the widely accepted criteria for malnutrition, the Global Leadership Initiative on Malnutrition (GLIM), a lot of metrics can be used to define the phenotypic and etiological criteria. To identify muscle mass reduction, anthropometric parameters such as calf circumference (CC) and hand grip strength (HGS) are preferable to other expensive methods in many situations because they are easy and inexpensive to measure, but their applicability needs to be verified in specific clinical scenarios. This study aims to verify the value of CC- and HGS-identified muscle loss in diagnosing malnutrition and predicting in-hospital complications (IHC) and prolonged length of hospital stay (PLOS) in elderly inpatients using machine learning methods. A sample of 7122 elderly inpatients who were enrolled in a previous multicenter cohort study in China were screened for eligibility for the current study and were then retrospectively diagnosed for malnutrition using 33 GLIM criteria that differ in their combinations of phenotypic and etiological criteria, in which CC or CC+HGS were used to identify muscle mass reduction. The diagnostic consistency with the subjective global assessment (SGA) criteria at admission was evaluated according to Kappa coefficients. The association and the predictive value of the GLIM-defined malnutrition with 30-day IHC and PLOS were evaluated with logistic regression and randomized forest models. In total, 2526 inpatients (average age 74.63 ± 7.12 years) were enrolled in the current study. The prevalence of malnutrition identified by the 33 criteria combinations ranged from 3.3% to 27.2%. The main IHCs was infectious complications (2.5%). The Kappa coefficients ranged from 0.130 to 0.866. Logistic regression revealed that malnutrition was identified by 31 GLIM criteria combinations that were significantly associated with 30-day IHC, and 22 were significantly associated with PLOS. Random forest prediction revealed that GLIM 15 (unconscious weight loss + muscle mass reduction, combined with disease burden/inflammation) performs best in predicting IHC; GLIM 30 (unconscious weight loss + muscle mass reduction + BMI reduction, combined with disease burden/inflammation) performs best in predicting PLOS. Importantly, CC alone performs better than CC+HGS in the criteria combinations for predicting adverse clinical outcomes. Muscle mass reduction defined by a reduced CC performs well in the GLIM criteria combinations for diagnosing malnutrition and predicting IHC and PLOS in elderly Asian inpatients. The applicability of other anthropometric parameters in these applications needs to be further explored.