Predictive Value For In-hospital Mortality Research Articles

Predictive Value of Glucose Coefficient of Variation for In-Hospital Mortality in Acute Myocardial Infarction Patients Undergoing PCI: Insights from the MIMIC-IV Database

BackgroundAcute myocardial infarction (AMI) carries a high short-term risk of death, even after percutaneous coronary intervention (PCI). Glucose variability (GV), measured by the glucose coefficient of variation (GluCV), is a potential risk factor for adverse outcomes. This study investigates GluCV's predictive value for in-hospital mortality in AMI patients undergoing PCI. MethodThis study involved 2,325 AMI patients who were admitted to the ICU and underwent PCI from the MIMIC-IV database. Patients were categorized into quartiles based on GluCV: <0.13, 0.13-0.20, 0.20-0.29, and ≥0.29. Multivariable logistic regression and Restricted cubic spline (RCS) analysis were employed to analyze the relationship between GluCV and in-hospital mortality. Mediation analysis was used to evaluate the role of GluCV in the relationship between disease complexity and severity. ResultsAmong the 2,325 patients, 203 (8.7%) died during hospitalization. Higher GluCV was associated with increased in-hospital mortality. Adjusted odds ratios for mortality were 1.35 (95% CI: 0.71-2.55), 1.91 (95% CI: 1.04-3.51), and 3.32 (95% CI: 1.83-6.02) for the second, third, and fourth groups, respectively. RCS analysis indicated a linear relationship between Log GluCV and mortality risk, with each 1 SD increase in Log GluCV associated with a 1.70-fold increase in mortality. Subgroup analysis showed a stronger relationship between GluCV and mortality in patients younger than 70. Mediation analysis indicated that GluCV partially mediates the effect of comorbidities on organ dysfunction. ConclusionsGluCV is an important predictor of in-hospital mortality in AMI patients undergoing PCI. Managing GV to minimize fluctuations may improve patient prognosis.

Impact of D-dimer on in-hospital mortality following aortic dissection: A systematic review and meta-analysis.

The utility of D-dimer (DD) as a biomarker for acute aortic dissection (AD) is recognized. Yet, its predictive value for in-hospital mortality remains uncertain and subject to conflicting evidence. To conduct a meta-analysis of AD-related in-hospital mortality (ADIM) with elevated DD levels. We searched PubMed, Scopus, Embase, and Google Scholar for AD and ADIM literature through May 2022. Heterogeneity was assessed using I 2 statistics and effect size (hazard or odds ratio) analysis with random-effects models. Sample size, study type, and patients' mean age were used for subgroup analysis. The significance threshold was P < 0.05. Thirteen studies (3628 patients) were included in our study. The pooled prevalence of ADIM was 20% (95%CI: 15%-25%). Despite comparable demographic characteristics and comorbidities, elevated DD values were associated with higher ADIM risk (unadjusted effect size: 1.94, 95%CI: 1.34-2.8; adjusted effect size: 1.12, 95%CI: 1.05-1.19, P < 0.01). Studies involving patients with a mean age of < 60 years exhibited an increased mortality risk (effect size: 1.43, 95%CI: 1.23-1.67, P < 0.01), whereas no significant difference was observed in studies with a mean age > 60 years. Prospective and larger sample size studies (n > 250) demonstrated a heightened likelihood of ADIM associated with elevated DD levels (effect size: 2.57, 95%CI: 1.30-5.08, P < 0.01 vs effect size: 1.05, 95%CI: 1.00-1.11, P = 0.05, respectively). Our meta-analysis shows elevated DD increases in-hospital mortality risk in AD patients, highlighting the need for larger, prospective studies to improve risk prediction models.

Association between the neutrophil-to-lymphocyte ratio and in-hospital mortality in patients with chronic kidney disease and coronary artery disease in the intensive care unit

BackgroundThe objective of this study was to investigate the correlation between neutrophil-to-lymphocyte ratios (NLR) and the risk of in-hospital death in patients admitted to the intensive care unit (ICU) with both chronic kidney disease (CKD) and coronary artery disease (CAD).MethodsData from the MIMIC-IV database, which includes a vast collection of more than 50,000 ICU admissions occurring between 2008 and 2019, was utilized in the study and eICU-CRD was conducted for external verification. The Boruta algorithm was employed for feature selection. Univariable and multivariable logistic regression analyses and multivariate restricted cubic spline regression were employed to scrutinize the association between NLR and in-hospital mortality. The receiver operating characteristic (ROC) curves were conducted to estimate the predictive ability of NLR.ResultsAfter carefully applying criteria to include and exclude participants, a total of 2254 patients with CKD and CAD were included in the research. The findings showed a median NLR of 7.3 (4.4, 12.1). The outcomes of multivariable logistic regression demonstrated that NLR significantly elevated the risk of in-hospital mortality (OR 2.122, 95% confidence interval [CI] 1.542–2.921, P < 0.001) after accounting for all relevant factors. Further insights from subgroup analyses unveiled that age and Sequential Organ Failure Assessment (SOFA) scores displayed an interactive effect in the correlation between NLR and in-hospital deaths. The NLR combined with traditional cardiovascular risk factors showed relatively great predictive value for in-hospital mortality (AUC 0.750).ConclusionThe findings of this research indicate that the NLR can be used as an indicator for predicting the likelihood of death during a patient’s stay in the intensive care unit, particularly for individuals with both CAD and CKD. The results indicate that NLR may serve as a valuable tool for assessing and managing risks in this group at high risk. Further investigation is required to authenticate these findings and investigate the mechanisms that underlie the correlation between NLR and mortality in individuals with CAD and CKD.

Shock index creatinine: a new predictor of mortality in acute coronary syndrome patients

BackgroundThe Shock Index Creatinine (SIC) scoring is a recently developed tool for risk stratification patients. These updated scoring was already used in ST-Elevation Myocardial Infarction (STEMI) patients. However its utility in predicting outcomes for patients with Acute Coronary Syndrome (ACS) remains unclear. This study aims to evaluate and update the current SIC score to predict in-hospital mortality among patients with ACS.Patients and methodsA retrospective cohort, Single-centered study enrolled 1349 ACS patients aged ≥ 18 years old diagnosed with ACS was conducted between January 2018 to January 2022 who met for inclusion and exclusion criteria. Study subjects were analyzed for in-hospital mortality and evaluated using binary linear regression analysis. The area under the curve (AUC) of SIC score was obtain to predict the sensitivity and specificity.ResultsMultivariate analysis showed that SIC score was significantly associated with in-hospital mortality. High SIC score (SIC ≥ 25) had significantly higher in-hospital mortality (p < 0.001) with odds ratio for (95% CIs) were 2.655 (1.6–4.31). Receiver operating characteristics (ROC) curve analysis determine the predictive power of SIC score for in-hospital mortality. SIC had an acceptable predictive value for in-hospital mortality (AUC = 0.789, 95% CI: 0.748–0.831, p < 0.001). The SIC score for sensitivity and specificity were, respectively, 71.5% and 74.4%, with optimal cutoff of SIC ≥ 25.ConclusionSIC had acceptable predictive value for in-hospital mortality in patients with all ACS spectrums. SIC was a useful parameter for predicting in-hospital mortality, particularly with a score ≥ 25. This is the first study to evaluate SIC in all spectrums of ACS.

High CRP/PNI levels predict an unfavorable prognosis in severe fever with thrombocytopenia syndrome: A propensity score matching study.

This study aimed to identify a novel inflammatory index and construct a nomogram for predicting in-hospital mortality due to severe fever with thrombocytopenia syndrome (SFTS). This cohort included 610 patients with SFTS hospitalized in Wuhan Union Hospital between March 2017 and November 2022. The ratio of C-reactive protein (CRP) to the prognostic nutritional index (PNI) was calculated and used to reflect patients' inflammatory status. Propensity score matching (PSM) was utilized to balance confounding factors between the low- and high-CRP/PNI groups. SFTS individuals from Jinyinhu Hospital were used as the validation cohort. Patients with SFTS and high CRP/PNI were significantly correlated with a higher percentage of severe and critical SFTS types and higher in-hospital mortality rates than those with low CRP/PNI. CRP/PNI was the potent risk indicator for in-hospital mortality in individuals with SFTS. The CRP/PNI nomogram showed a good predictive value for in-hospital mortality in patients with SFTS. After PSM, the predictive performance of CRP/PNI for 28-day mortality was excellent. Finally, the CRP/PNI could still assess patients with SFTS at different risks based on SFTS data from another medical center. The CPR/PNI ratio exhibited a strong positive correlation with the SFTS disease type and could predict in-hospital mortality in the early stages of SFTS. The CPR/PNI ratio could substantially help clinicians facilitate the early identification of patients with high-risk SFTS and the timely initiation of intensive therapy.

Predictive Significance of the ABC Score for Early Re-Hemorrhage and In-Hospital Mortality in High-Risk Variceal Bleeding among Cirrhotic Patients

(1) Background: Upper gastrointestinal bleeding due to ruptured varices is a severe complication in patients with cirrhosis, with high rates of recurrent hemorrhage and in-hospital mortality. This study aimed to evaluate the value of the ABC score in predicting two events among 201 cirrhotic patients with high-risk variceal hemorrhage. (2) Methods: The ABC score was calculated and categorized into risk groups of patients, and the association between the ABC score and the rates of early hemorrhagic recurrence and clinic mortality were analyzed. (3) Results: Among 201 patients, 8.0% experienced early rebleeding within five days of admission, and 10.4% died in the hospital. Patients who experienced events had higher average ABC scores compared to those who did not experience these events (p &lt; 0.001), especially in the high-risk group (with ABC score ≥ 8). The ABC score showed an excellent predictive value for in-hospital mortality with an AUROC of 0.804, with the optimal cutoff point being 8 points. Additionally, the ABC score demonstrated an acceptable predictive value for early rebleeding with an AUROC of 0.744, and the best cutoff point was 9 points. (4) Conclusions: The ABC score is closely associated with the rates of early re-hemorrhage and in-hospital mortality in cirrhotic patients with variceal bleeding. This scoring system has the potential for clinical application, aiding in early risk stratification for recurrent bleeding and mortality and allowing for more aggressive interventions in high-risk cases.

LUV Score - a combination of bedside lung ultrasound and velocity-time integral (VTI) to predict mortality risk in STEMI patients

Abstract Background Early risk stratification is essential for in-hospital management of ST-segment–elevation myocardial infarction (STEMI). Development of acute heart failure and pulmonary edema is associated with poorer prognosis in this setting. Velocity–time integral (VTI) estimates stroke volume and cardiac output and has never been tested in acute coronary syndromes. Purpose Our aim was to evaluate the prognostic ability of a novel cardiothoracic ultrasound protocol for hemodynamic assessment with lung ultrasound (LUS) and VTI in patients with STEMI. Methods LUS and VTI were performed within 24h of admission for STEMI. LUS consisted of 8 scanning zones. A LUS combined with VTI (LUV) classification was developed. Receiver operating characteristic (ROC) analyses were performed to assess LUV score with in-hospital mortality, and it was compared with traditional Killip classification. Results From September 2022 and January 2023, 104 consecutive patients were admitted with ST-segment–elevation myocardial infarction. Fifteen patients were excluded from the analysis because were not assessed within 24h of admission. Therefore, 89 patients were included in the final analysis. Mean age was 62 years, 59% were male, 55% had hypertension, 32% had diabetes, 43% was anterior wall myocardial infarction and 16% had Killip 3 or 4 at admission. Overall in-hospital mortality was 7.9% and mortality across LUV categories were 0%, 0%, 8% and 50% for LUV A-D, respectively. A VTI ≥ 14 implied a negative predictive value for in-hospital mortality of 100%. The area under the ROC curve of LUV classification for in-hospital mortality was 0.92 (P=0.001), and Killip classification (0.85, P&amp;lt;0.001; P=0.48 for the difference between curves – Figure 1). Incidences of culprit vessel according to LUV categories are illustrated at Figure 2. There were no left main culprit vessel in our sample. The distribution of Cardiac Output (CO) and Cardiac index (CI) and Cardiac Power Output (CPO) were the following: LUV A: CO = 4.5 (1.2), CI = 4.0 (1.1) CPO = 0.9 (0.3); LUV B CO = 3.8 (0.9) CI = 3.4 (0.8) CPO = 0.7 (0.2); LUV C CO = 3.3 (0.8) CI = 3.0 (0.8) CPO = 0.6 (0.1) ; LUV D CO = 2.9 (0.7) CI = 2.7(0.5) CPO = 0.5(0.1). Conclusions In a cohort of patients with ST-segment–elevation myocardial infarction undergoing primary percutaneous coronary intervention, LUV score provided an excellent AUC for prediction of in-hospital mortality. LUV score is an easy, noninvasive, rapidly acquired, and accurate method for risk stratification in STEMI patients.

The impact of fasting stress hyperglycemia ratio, fasting plasma glucose and hemoglobin A1c on in-hospital mortality in patients with and without diabetes: findings from the China acute myocardial infarction registry

BackgroundStress hyperglycemia was positively associated with poor prognosis in individuals with acute myocardial infarction (AMI). However, admission glucose and stress hyperglycemia ratio (SHR) may not be the best indicator of stress hyperglycemia. We performed this study to evaluate the comparative prognostic value of different measures of hyperglycemia (fasting SHR, fasting plasma glucose [FPG], and hemoglobin A1c [HbA1c]) for in-hospital mortality in AMI patients with or without diabetes.MethodsIn this prospective, nationwide, multicenter China Acute Myocardial Infarction (CAMI) registry, 5,308 AMI patients including 2081 with diabetes and 3227 without diabetes were evaluated. Fasting SHR was calculated using the formula [(first FPG (mmol/l))/(1.59×HbA1c (%)-2.59)]. According to the quartiles of fasting SHR, FPG and HbA1c, diabetic and non-diabetic patients were divided into four groups, respectively. The primary endpoint was in-hospital mortality.ResultsOverall, 225 (4.2%) patients died during hospitalization. Individuals in quartile 4 had a significantly higher rate of in-hospital mortality compared with those in quartile 1 in diabetic cohort (9.7% vs. 2.0%; adjusted odds ratio [OR] 4.070, 95% CI 2.014–8.228) and nondiabetic cohort (8.8% vs. 2.2%; adjusted OR 2.976, 95% CI 1.695–5.224). Fasting SHR was also correlated with higher in-hospital mortality when treated as a continuous variable in diabetic and nondiabetic patients. Similar results were observed for FPG either as a continuous variable or a categorical variable. In addition, fasting SHR and FPG, rather than HbA1c, had a moderate predictive value for in-hospital mortality in patients with diabetes (areas under the curve [AUC] for fasting SHR: 0.702; FPG: 0.689) and without diabetes (AUC for fasting SHR: 0.690; FPG: 0.693). The AUC for fasting SHR was not significantly different from that of FPG in diabetic and nondiabetic patients. Moreover, adding fasting SHR or FPG to the original model led to a significant improvement in C-statistic regardless of diabetic status.ConclusionsThis study indicated that, in individuals with AMI, fasting SHR as well as FPG was strongly associated with in-hospital mortality regardless of glucose metabolism status. Fasting SHR and FPG might be considered as a useful marker for risk stratification in this population.Trial registration: ClinicalTrials.gov NCT01874691.

Comparison of Different Risk Scores for Prediction of In-Hospital Mortality in STEMI Patients Treated with PPCI.

Several risk scores have been developed to predict and analyze in-hospital mortality and short- and long-term outcomes of ST-elevation myocardial infarction (STEMI) patients after primary percutaneous coronary intervention (PPCI); these can classify patients as having a high or low risk of death or complications. To compare the prognostic precision of four risk scores for predicting in-hospital mortality in patients with STEMI treated with PPCI. We performed a retrospective cohort analysis of patients with STEMI who underwent PPCI between 2012 and 2019 (N = 1346). GRACE (Global Registry of Acute Cardiac Events), CADILLAC (Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications), Zwolle, and TIMI (Thrombolysis in Myocardial Infarction) risk scores were calculated for each patient according to different variables. We evaluated the predictive accuracy of these scores for in-hospital mortality using the C statistic, which was obtained using logistic regression and receiver operating characteristic curves. The GRACE, CADILLAC, Zwolle, and TIMI risk scores all had good predictive precision for in-hospital mortality, with C statistics ranging from 0.842 to 0.923. The GRACE and CADILLAC risk scores were found to be superior. All GRACE, CADILLAC, Zwolle, and TIMI risk scores showed a high predictive value for in-hospital mortality due to all causes in patients with STEMI treated with PPCI. The GRACE and CADILLAC risk scores revealed a better accuracy for predicting in-hospital mortality than the Zwolle and TIMI risk scores.

The Norton scale is an important predictor of in-hospital mortality in internal medicine patients.

The Norton scale, a marker of patient frailty used to predict the risk of pressure ulcers, but the predictive value of the Norton scale for in-hospital mortality after adjustment for a wide range of demographic, and abnormal admission laboratory test results shown in themselves to have a high predictive value for in-hospital mortality is unclear. The study aims to determine the value of the Norton scale and the presence of a urinary catheter in predicting in hospital mortality. The study population included all acutely admitted adult patients in 2020 through October 2021 to one of three internal medicine departments at the Laniado Hospital, a regional hospital with 400 beds in Israel. The main objective was to (a) identify the variables associated with the Norton Scale and (b) determine whether it predicts in-hospital mortality after adjustment for these variables. The Norton scale was associated with an older age, female gender, presence of a urinary catheter, and abnormal laboratory tests. The odds of in-hospital mortality in those with intermediate, high, and very high Norton scale risk groups were 3.10 (2.23-3.56), 6.48 (4.02-10.46), and 12.27 (7.37-20.44), respectively, after adjustment for the remaining predictors. Adding the Norton scale and the presence of a urinary catheter to the prediction logistic regression model that included age, gender, and abnormal laboratory test results increased the c-statistic from 0.870 (0.864-0.876) to 0.908 (0.902-0.913). The Norton scale and presence of a urinary catheter are important predictors of in-hospital mortality in acutely hospitalized adults in internal medicine departments.

Simplified acute physiology score III is excellent for predicting in-hospital mortality in coronary care unit patients with acute myocardial infarction: A retrospective study.

Coronary care unit (CCU) patients with acute myocardial infarction (AMI) lack effective predictors of in-hospital mortality. This study aimed to investigate the performance of four scoring systems in predicting in-hospital mortality in CCU patients with AMI. The baseline data, the logistic organ dysfunction system (LODS), the Oxford acute severity of illness score (OASIS), the simplified acute physiology score II (SAPS II), and the simplified acute physiology score III (SAPS III) scores of the patients were extracted from the fourth edition of the Medical Information Mart for Critical Care (MIMIC-IV) database. Independent risk factors for in-hospital mortality were identified by regression analysis. We performed receiver operating characteristic (ROC) curves and compared the area under the curve (AUC) to clarify the predictive value of the four scoring systems. Meanwhile, Kaplan-Meier curves and decision curve analysis (DCA) were performed to determine the optimal scoring system for predicting in-hospital mortality. A total of 1,098 patients were included. The SAPS III was an independent risk factor for predicting in-hospital mortality in CCU patients with AMI before and after the propensity score matching (PSM) analysis. The discrimination of in-hospital mortality by SAPS III was superior to that of LODS, OASIS, and SAPS II. The AUC of the SAPS III scoring system was the highest among the four scoring systems, at 0.901 (before PSM) and 0.736 (after PSM). Survival analysis showed that significantly more in-hospital mortality occurred in the high-score SAPS III group compared to the low-score SAPS III group before PSM (HR 7.636, P < 0.001) and after PSM (HR 2.077, P = 0.005). The DCA curve of SAPS III had the greatest benefit score across the largest threshold range compared to the other three scoring systems. The SAPS III was an independent risk factor for predicting in-hospital mortality in CCU patients with AMI. The predictive value for in-hospital mortality with SAPS III is superior to that of LODS, OASIS, and SAPS II. The results of the DCA analysis suggest that SAPS III may provide a better clinical benefit for patients. We demonstrated that SAPS III is an excellent scoring system for predicting in-hospital mortality for CCU patients with AMI.

Apyrexia improves the prognostic value of quick SOFA in older patients with acute pneumonia or bacteremic urinary tract infection.

Apyrexia is increasingly recognized as an indicator of inadequate inflammatory response in older patients with suspected infection. We aimed to evaluate whether temperature at admission could improve the prognostic value of the Quick Sequential Organ Failure Assessment (qSOFA) for predicting in-hospital mortality after acute infection. We created a new score, named qSOFAGE (qSOFA in GEriatrics), by adding apyrexia as an item to the existing qSOFA (+ 1 point if temperature at admission ≤ 38°C). We compared the prognostic value of the qSOFA, the qSOFAGE and temperature at admission for predicting in-hospital mortality after acute infection in two cohorts including older patients with acute pneumonia (AP) or bacteremic urinary tract infection (UTI). 217 consecutive patients aged ≥ 75 hospitalized for AP (first cohort) and 105 for bacteremic UTI (second cohort) were recorded. Temperature at admission was strongly inversely correlated with in-hospital mortality in both cohorts (Odds Ratios per °C (95% Confidence Interval): 0.60 (0.45-0.80) and 0.46 (0.27-0.79) for AP and UTI. respectively). Adding the temperature ≤ 38°C item to the qSOFA markedly improved its predictive value for in-hospital mortality in the two groups: C-statistics for qSOFAGE vs. qSOFA: 0.63 (0.53-0.73) vs. 0.56 (0.46-0.67) in AP cohort; 0.74 (0.58-0.89) vs. 0.69 (0.53-0.85) in UTI cohort. For patients with qSOFAGE ≥ 3, in-hospital mortality reached 37% after AP and 55% after bacteremic UTI. Temperature at admission was strongly correlated with mortality in these two cohorts of older patients hospitalized for acute infection. The next step will be to validate this score in cohorts of older patients with suspected infection.

Retrospective External Validation of the Status Epilepticus Severity Score (STESS) to Predict In-hospital Mortality in Adults with Nonhypoxic Status Epilepticus: A Machine Learning Analysis.

The objective of this study was to validate the value of the Status Epilepticus Severity Score (STESS) in the prediction of the risk of in-hospital mortality in patients with nonhypoxic status epilepticus (SE) using a machine learning analysis. We included consecutive patients with nonhypoxic SE (aged ≥ 16years) admitted from 2013 to 2021 at the Modena Academic Hospital. A decision tree analysis was performed using in-hospital mortality as a dependent variable and the STESS predictors as input variables. We evaluated the accuracy of STESS in predicting in-hospital mortality using the area under the receiver operating characteristic curve (AUROC) with 95% confidence interval (CI). Among 629 patients with SE, the in-hospital mortality rate was 23.4% (147 of 629). The median STESS in the entire cohort was 2.9 (SD 1.6); it was lower in surviving compared with deceased patients (2.7, SD 1.5 versus 3.9, SD 1.6; p < 0.001). Of deceased patients, 82.3% (121 of 147) had scores of 3-6, whereas 17.7% (26 of 147) had scores of 0-2 (p < 0.001). STESS was accurate in predicting mortality, with an AUROC of 0.688 (95% CI 0.641-0.734) only slightly reduced after bootstrap resampling. The most significant predictor was the seizure type, followed by age and level of consciousness at SE onset. Nonconvulsive SE in coma and age ≥ 65years predicted a higher risk of mortality, whereas generalized convulsive SE and age < 65years were associated with a lower risk of death. The decision tree analysis using STESS variables correctly classified 90% of survivors and 34% of nonsurvivors after the SE, with an overall risk of error of 23.1%. This validation study using a machine learning system showed that STESS is a valuable prognostic tool. The score appears particularly accurate and effective in identifying patients who are alive at discharge (high negative predictive value), whereas it has a lower predictive value for in-hospital mortality.

Diagnostic value of early bedside ultrasound measurement of quadriceps femoris on in-hospital mortality of septic patients

To investigate the changes of quadriceps femoris thickness with the length of stay in intensive care unit (ICU) in patients with sepsis, and to evaluate the diagnostic value of muscle changes in mortality. A prospective study was conducted, and 92 patients with sepsis who were admitted to the ICU of the Affiliated Hospital of Jining Medical College from January 2020 to December 2021 were enrolled. The thickness of quadriceps femoris [including the quadriceps femoris muscle thickness at the midpoint of the anterior superior iliac spine and the upper edge of the patella (M-QMLT), and at the middle and lower 1/3 of the patella (T-QMLT)] measured by ultrasound 1 day (D1), 3 days (D3), and 7 days (D7) after admission to the ICU were collected. The atrophy rate of quadriceps femoris was calculated 3 and 7 days after admission to the ICU compared with 1 day [(D3-D1)/D1 and (D7-D1)/D1, (TD3-TD1)/TD1 and (TD7-TD1)/TD1, respectively]. The demographic information, underlying diseases, vital signs when admission to the ICU and in-hospital mortality of all patients were recorded, and the differences of the above indicators between the two groupswere compared. Multivariate Logistic regression was used to analyze the influence of quadriceps femoris muscle thickness and atrophy rate on in-hospital mortality of septic patients. The receiver operator characteristic curve (ROC curve) was drawn to analyze the predictive value of quadriceps femoris muscle thickness and atrophy rate on in-hospital mortality of septic patients. A total of 92 patients with severe sepsis were included, of which 41 patients died in hospital, 51 patients discharged. The in-hospital mortality was 44.6%. The muscle thickness of quadriceps femoris in severe septic patients decreased with the prolongation of ICU stay, and there was no significant difference between the two groups at the first and third day of ICU admission. The muscle thickness of quadriceps femoris at different measuring positions in the survival group was significantly greater than those in the death group 7 days after admission to the ICU [M-QMLT D7 (cm): 0.50±0.26 vs. 0.39±0.19, T-QMLT D7 (cm): 0.58±0.29 vs. 0.45±0.21, both P < 0.05]. The atrophy rate of quadriceps femoris muscle thickness at different measuring positions 3 and 7 days after admission to ICU in the survival group was significantly lower than those in the death group [(D3-D1)/D1: (8.33±3.44)% vs. (9.74±3.91)%, (D7-D1)/D1: (12.21±4.76)% vs. (19.80±6.15)%, (TD3-TD1)/TD1: (7.83±4.26)% vs. (10.51±4.75)%, (TD7-TD1)/TD1: (11.10±5.46)% vs. (20.22±6.05)%, all P < 0.05]. Multivariate Logistic regression analysis showed that M-QMLT D7, T-QMLT D7, (D3-D1)/D1, (D7-D1)/D1, (TD3-TD1)/TD1, (TD7-TD1)/TD1 were independent risk factors for in-hospital mortality (all P < 0.05). The results were stable after adjusting for confounding factors. ROC curve analysis showed that (TD7-TD1)/TD1 [area under the ROC curve (AUC) was 0.853, 95% confidence interval (95%CI) was 0.773-0.934] was superior to (D7-D1)/D1, T-QMLT D7, M-QMLT D7, (TD3-TD1)/TD1 and (D3-D1)/D1 [AUC was 0.821 (0.725-0.917), 0.692 (0.582-0.802), 0.683 (0.573-0.794), 0.680 (0.569-0.791), 0.622 (0.502-0.742)]. For septic patients in ICU, bedside ultrasound monitoring of quadriceps femoris muscle thickness and atrophy rate has a certain predictive value for in-hospital mortality, and a certain guiding significance in clinical treatment and predicting the prognosis of sepsis.

TH6.12 Predicting Mortality and Morbidity in Oesophageal perforation: A Review of Current Tools

Abstract Aim Oesophageal perforation is a challenging condition associated with high mortality and morbidity. There is a lack of consensus regarding the optimal treatment strategy, when and whom to operate on. The aim of this study is to identify the optimal morbidity and mortality risk prediction tool for patients with oesophageal perforation. Methods We compared and validated commonly used risk prediction models, including the Pittsburgh Severity Score (PSS), the National Emergency Laparotomy Audit score (NELA score), the Portsmouth Physiological and Operative Severity Score for the enumeration of Mortality (P-POSSUM), and the Surgical Outcome Risk Tool (SORT) using a dataset of 83 patients ranging from 2009 to 2021. The ability to predict morbidity was assessed using the comprehensive complication index (CCI). The CCI was calculated for both operative and non-operative cohorts. Results Of the scores assessed, NELA showed the most robust predictive value for in-hospital mortality, 30-day mortality, and 90 mortality (AUROC 0.812, 0.8602, 0.8302, respectively). The PSS also showed significant predictive value for in-hospital mortality, 30-day mortality, and 90 mortality (AUROC 0.792, 0.856, 0.813 respectively). Furthermore, NELA had the strongest correlation between score and CCI (rs 0.644 p&amp;lt;0.001). Conclusion Despite not being validated for oesophageal pathology, NELA appears to be the optimum scoring model to predict mortality and morbidity for this patient population. This is the first study to compare the efficacy of different risk prediction models in oesophageal perforations and could be used to inform shared decision making and peri-operative outcomes. Further large-scale validation of risk prediction tools is required to corroborate these findings.

Mortality prediction in pediatric postcardiotomy veno-arterial extracorporeal membrane oxygenation: A comparison of scoring systems.

BackgroundPediatric postcardiotomy veno-arterial extracorporeal membrane oxygenation (VA-ECMO) patients have high mortality and morbidity. There are currently three scoring systems available to predict mortality: the Pediatric Extracorporeal Membrane Oxygenation Prediction (PEP) model, Precannulation Pediatric Survival After VA-ECMO (Pedi-SAVE) score, and Postcannulation Pedi-SAVE score. These methods provide risk stratification scores for pediatric patients requiring ECMO for cardiac support. However, comparative validation of these scoring systems remains scarce. We aim to assess the ability of these models to predict outcomes in a cohort of pediatric patients undergoing VA-ECMO after cardiac surgery, and identify predictors of in-hospital mortality.MethodsA retrospective analysis of 101 children admitted to Fuwai Hospital who received VA-ECMO from January 1, 2010 to December 31, 2020 was performed. Patients were divided into two groups, survivors (n = 49) and non-survivors (n = 52) according to in-hospital mortality. PEP model and Pedi-SAVE scores were calculated. The primary outcomes were the risk factors of in-hospital mortality, and the ability of the PEP model, Precannulation Pedi-SAVE and Postcannulation Pedi-SAVE scores to predict in-hospital mortality.ResultsPostcannulation Pedi-SAVE score accessing the entire ECMO process had the greatest area under receiver operator curve (AUROC), 0.816 [95% confidence interval (CI): 0.733–0.899]. Pre-ECMO PEP model could predict in-hospital mortality [AUROC = 0.691 (95% CI: 0.565–0.817)], and Precannulation Pedi-SAVE score had the poorest prediction [AUROC = 0.582(95% CI: 0.471–0.694)]. Lactate value at ECMO implantation [OR = 1.199 (1.064–1.351), P = 0.003] and infectious complications [OR = 5.169 (1.652–16.172), P = 0.005] were independent risk factors for in-hospital mortality.ConclusionPediatric cardiac ECMO scoring systems, including multiple risk factors before and during ECMO, were found to be useful in this cohort. Both the pre-ECMO PEP model and the Postcannulation Pedi-SAVE score were found to have high predictive value for in-hospital mortality in pediatric postcardiotomy VA-ECMO.

Independent predictors of in-hospital mortality and the need for intensive care in hospitalized non-critical COVID-19 patients: a prospective cohort study

One of the most helpful strategies to deal with ongoing coronavirus pandemics is to use some prudence when treating patients infected with SARS-CoV-2. We aimed to evaluate the clinical, demographic, and laboratory parameters that might have predictive value for in-hospital mortality and the need for intensive care and build a model based on them. This study was a prospective, observational, single-center study including non-critical patients admitted to COVID-19 wards. Besides classical clinic-demographic features, basic laboratory parameters obtained on admission were tested, and then new models for each outcome were developed built on the most significant variables. Receiver operating characteristics (ROC) analyses were performed by calculating each model’s probability. A total of 368 non-critical hospitalized patients were recruited, the need for ICU care was observed in 70 patients (19%). The total number of patients who died in either ICU or wards was 39 (10.6%). The first two models (based on clinical features and demographics) were developed to predict ICU and death, respectively; older age, male sex, active cancer, and low baseline saturation were noted to be independent predictors. The area under the curve values of the first two models were noted 0.878 and 0.882 (p < .001; confidence interval [CI] 95% [0.837–0.919], p < .001; CI 95% [0.844–0.922]). Following two models, the third and fourth were based on laboratory parameters with clinic-demographic features. Initial lower sodium and lower albumin levels were determined as independent factors in predicting the need for ICU care; higher blood urea nitrogen and lower albumin were independent factors in predicting in-hospital mortality. The area under the curve values of the third and fourth model was noted 0.938 and 0.929, respectively (p < .001; CI 95% [0.912–0.965], p < .001; CI 95% [0.895–962]). By integrating the widely available blood tests results with simple clinic demographic data, non-critical patients can be stratified according to their risk level. Such stratification is essential to filter the patients’ non-critical underlying diseases and conditions that can obfuscate the physician’s predictive capacity.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11739-022-02962-6.

Serum Bicarbonate: Reconsidering the Importance of a Neglected Biomarker in Predicting Clinical Outcomes in Sepsis.

BackgroundDespite being an important pathophysiological component, information on the predictive value of serum bicarbonate level in sepsis is limited.Study design and methodThis is a single-centered retrospective study involving 4176 patients admitted to the medical ICU (MICU) with a diagnosis of sepsis. Patients were divided into two groups based on the presence or absence of chronic kidney disease (CKD) on admission: CKD and non-CKD, respectively. Each group was then divided into three sub-groups based on serum bicarbonate level at presentation (in mEq/l)- low (<22), normal (22-28), and high (>28). We compared the clinical outcomes between the sub-groups in each group, with in-hospital mortality as the primary endpoint. Secondary endpoints included vasopressor-free days, ventilator-free days, ICU-free days, and hospital-free days.ResultIn both the CKD and non-CKD groups, low serum bicarbonate was associated with significantly increased in-hospital mortality. There was no difference in the mortality between the sub-groups with normal and high serum bicarbonate. When adjusted for other known predictors of mortality, the association of low serum bicarbonate with increased in-hospital mortality was statistically significant only in the patient group with a Sequential Organ Failure Assessment (SOFA) score of ≥9. Additionally, the SOFA score had a better predictive value for in-hospital mortality, ICU-free days, and ventilator-free days when the serum bicarbonate level was <22.InterpretationSerum bicarbonate is a good predictor of clinical outcomes in sepsis and can be used along with other markers of sepsis to predict clinical outcomes.

Comparison of the predictive value of the Oxford acute severity of illness score and simplified acute physiology score II for in-hospital mortality in intensive care unit patients with sepsis: an analysis based on MIMIC-IV database

To compare the predictive value of Oxford acute severity of illness score (OASIS) and simplified acute physiology score II (SAPS II) for in-hospital mortality in intensive care unit (ICU) patients with sepsis. A retrospective cohort study was conducted using the data in the Medical Information Mart for Intensive Care-IV 0.4 (MIMIC-IV 0.4). Based on Sepsis-3 diagnostic criteria, the basic information of ICU adult sepsis patients with infection and sequential organ failure assessment (SOFA) score ≥ 2 within 24 hours of ICU admission admitted for the first time in the database was extracted, including gender, age, vasopressor drugs, sedative drugs, mechanical ventilation, renal replacement therapy, length of ICU stay, OASIS, SAPS II scores, etc. The primary outcome was in-hospital mortality. A receiver operator characteristic curve (ROC curve) was drawn, and the area under the ROC curve (AUC) was calculated to compare the prognostic value of OASIS score and SAPS II score. A total of 11 098 adult ICU sepsis patients were enrolled in the final analysis, of which 2 320 died and 8 778 survived in hospital, with a mortality of 20.90%. Compared with the survivors, the non-survivors were older [years old: 71 (60, 81) vs. 67 (56, 78)], had longer length of ICU stay [days: 6.95 (3.39, 13.07) vs. 4.23 (2.19, 9.73)] and higher proportions of using vasopressor drugs, sedative drugs, mechanical ventilation and renal replacement therapy [vasopressor drugs: 50.65% (1 175/2 320) vs. 33.05% (2 901/8 778), sedative drugs: 58.53% (1 358/2 320) vs. 48.41% (4 249/8 778), mechanical ventilation: 89.57% (2 078/2 320) vs. 81.66% (7 168/8 778), renal replacement therapy: 11.98% (278/2 320) vs. 6.57% (577/8 778), all P < 0.01]. Moreover, the non-survivors had higher OASIS score [43 (36, 49) vs. 35 (29, 41), P < 0.01] and SAPS II score [49 (40, 60) vs. 38 (31, 47), P < 0.01] as compared with the survivors. ROC curve analysis showed that the AUC of OASIS score and SAPS II score for predicting in-hospital death of ICU patients with sepsis was 0.713 [95% confidence interval (95%CI) was 0.701-0.725] and 0.716 (95%CI was 0.704-0.728), respectively, and the Delong test showed no significant difference in AUC between the two scoring systems (P > 0.05). OASIS score has a good predictive value for in-hospital mortality in sepsis patients, which is similar to SAPS II score. OASIS score is simpler and has a broader clinical application prospect than SAPS II score.

Predictive value of machine learning for in-hospital mortality for trauma-induced acute respiratory distress syndrome patients: an analysis using the data from MIMIC III

To investigate the value of machine learning methods for predicting in-hospital mortality in trauma patients with acute respiratory distress syndrome (ARDS). A retrospective non-intervention case-control study was performed. Trauma patients with ARDS met the Berlin definition were extracted from the the Medical Information Mart for Intensive Care III (MIMIC III) database. The basic information [including gender, age, body mass index (BMI), pH, oxygenation index, laboratory indexes, length of stay in the intensive care unit (ICU), the proportion of mechanical ventilation (MV) or continuous renal replacement therapy (CRRT), acute physiology score III (APS III), sequential organ failure score (SOFA) and simplified acute physiology score II (SAPS II)], complications (including hypertension, diabetes, infection, acute hemorrhagic anemia, sepsis, shock, acidosis and pneumonia) and prognosis data of patients were collected. Multivariate Logistic regression analysis was used to screen meaningful variables (P < 0.05). Logistic regression model, XGBoost model and artificial neural network model were constructed, and the receiver operator characteristic curve (ROC) was performed to evaluate the predictive value of the three models for in-hospital mortality in trauma patients with ARDS. A total of 760 trauma patients with ARDS were enrolled, including 346 mild cases, 301 moderate cases and 113 severe cases; 618 cases survived and 142 cases died in hospital; 736 cases received MV and 65 cases received CRRT. Multivariate Logistic regression analysis screened out significant variables, including age [odds ratio (OR) = 1.035, 95% confidence interval (95%CI) was 1.020-1.050, P < 0.001], BMI (OR = 0.949, 95%CI was 0.917-0.983, P = 0.003), blood urea nitrogen (BUN; OR = 1.019, 95%CI was 1.004-1.033, P = 0.010), lactic acid (Lac; OR = 1.213, 95%CI was 1.124-1.309, P < 0.001), red cell volume distribution width (RDW; OR = 1.249, 95%CI was 1.102-1.416, P < 0.001), hematocrit (HCT, OR = 1.057, 95%CI was 1.019-1.097, P = 0.003), hypertension (OR = 0.614, 95%CI was 0.389-0.968, P = 0.036), infection (OR = 0.463, 95%CI was 0.289-0.741, P = 0.001), acute renal failure (OR = 2.021, 95%CI was 1.267-3.224, P = 0.003) and sepsis (OR = 2.105, 95%CI was 1.265-3.502, P = 0.004). All the above variables were used to construct the model. Logistic regression model, XGBoost model and artificial neural network model predicted in-hospital mortality with area under the curve (AUC) of 0.737 (95%CI was 0.659-0.815), 0.745 (95%CI was 0.672-0.819) and 0.757 (95%CI was 0.680-0.884), respectively. There was no significant difference between any two models (all P > 0.05). Logistic regression model, XGBoost model and artificial neural network model including age, BMI, BUN, Lac, RDW, HCT, hypertension, infection, acute renal failure and sepsis have good predictive value for in-hospital mortality of trauma patients with ARDS.