Simplified Acute Physiology Score II Research Articles

External Validation of the PREdiction of Survival on Extracorporeal Membrane Oxygenation Therapy (PRESET) Score: A Single-Center Cohort Experience.

Acute respiratory distress syndrome (ARDS) is a life-threatening condition affecting >10% of intensive care unit (ICU) patients worldwide with a mortality of up to 59% depending on severity. Extracorporeal membrane oxygenation (ECMO) is a potentially life-saving procedure in severe ARDS but is technically and financially challenging. In recent years, various scoring systems have been proposed to select patients most likely to benefit from ECMO, with the PREdiction of Survival on ECMO Therapy (PRESET) score being one of the most used. We collected data from 283 patients with ARDS of various etiology who underwent veno-venous (V-V) ECMO therapy at a German tertiary care ICU from January 2012 to December 2022. Median age in the cohort was 56 years, and 64.31% were males. The in-hospital mortality rate was 50.88% (n = 144). The median (25%; 75% quartile) severity scores were 38 (31; 49) for Simplified Acute Physiology Score (SAPS) II, 12 (10; 13) for Sequential Organ Failure Assessment (SOFA) and 7 (5; 8) for PRESET. Simplified Acute Physiology Score-II displayed the best prognostic value (area under the receiver operating characteristic [AUROC]: 0.665 [confidence interval (CI): 0.574-0.756; p = 0.046]). Prediction performance was weak in all analyzed scores despite good calibration. Simplified Acute Physiology Score-II had the best discrimination after adjustment of our original cohort. The use of scores explored in this study for patient selection for eligibility for V-V ECMO is not recommendable.

Predictive nomogram for 28-day mortality risk in mitral valve disorder patients in the intensive care unit: A comprehensive assessment from the MIMIC-III database

BackgroundMitral valve disorder (MVD) stands as the most prevalent valvular heart disease. Presently, a comprehensive clinical index to predict mortality in MVD remains elusive. The aim of our study is to construct and assess a nomogram for predicting the 28-day mortality risk of MVD patients. MethodsPatients diagnosed with MVD were identified via ICD-9 code from the MIMIC-III database. Independent risk factors were identified utilizing the LASSO method and multivariate logistic regression to construct a nomogram model aimed at predicting the 28-day mortality risk. The nomogram's performance was assessed through various metrics including the area under the curve (AUC), calibration curves, Hosmer-Lemeshow test, integrated discriminant improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA). ResultsThe study encompassed a total of 2771 patients diagnosed with MVD. Logistic regression analysis identified several independent risk factors: age, anion gap, creatinine, glucose, blood urea nitrogen level (BUN), urine output, systolic blood pressure (SBP), respiratory rate, saturation of peripheral oxygen (SpO2), Glasgow Coma Scale score (GCS), and metastatic cancer. These factors were found to independently influence the 28-day mortality risk among patients with MVD. The calibration curve demonstrated adequate calibration of the nomogram. Furthermore, the nomogram exhibited favorable discrimination in both the training and validation cohorts. The calculations of IDI, NRI, and DCA analyses demonstrate that the nomogram model provides a greater net benefit compared to the Simplified Acute Physiology Score II (SAPSII), Acute Physiology Score III (APSIII), and Sequential Organ Failure Assessment (SOFA) scoring systems. ConclusionThis study successfully identified independent risk factors for 28-day mortality in patients with MVD. Additionally, a nomogram model was developed to predict mortality, offering potential assistance in enhancing the prognosis for MVD patients. It's helpful in persuading patients to receive early interventional catheterization treatment, for example, transcatheter mitral valve replacement (TMVR), transcatheter mitral valve implantation (TMVI).

A comparison of Simplified Acute Physiology Score II and Sepsis-related Organ Failure Assessment Score for prediction of mortality after Intensive Care Unit cardiac arrest.

This study investigates the predictive value and suitable cutoff values of the Sepsis-related Organ Failure Assessment Score (SOFA) and Simplified Acute Physiology Score II (SAPS-II) to predict mortality during or after Intensive Care Unit Cardiac Arrest (ICU-CA). In this secondary analysis the ICU database of a German university hospital with five ICU was screened for all ICU-CA between 2016-2019. SOFA and SAPS-II were used for prediction of mortality during ICU-CA, hospital-stay and one-year-mortality. Receiver operating characteristic curves (ROC), area under the ROC (AUROC) and its confidence intervals were calculated. If the AUROC was significant and considered "acceptable," cutoff values were determined for SOFA and SAPS-II by Youden Index. Odds ratios and sensitivity, specificity, positive and negative predictive values were calculated for the cutoff values. A total of 114 (78 male; mean age: 72.8±12.5 years) ICU-CA were observed out of 14,264 ICU-admissions (incidence: 0.8%; 95% CI: 0.7-1.0%). 29.8% (N.=34; 95% CI: 21.6-39.1%) died during ICU-CA. SOFA and SAPS-II were not predictive for mortality during ICU-CA (P>0.05). Hospital-mortality was 78.1% (N.=89; 95% CI: 69.3-85.3%). SAPS-II (recorded within 24 hours before and after ICU-CA) indicated a better discrimination between survival and death during hospital stay than SOFA (AUROC: 0.81 [95% CI: 0.70-0.92] vs. 0.70 [95% CI: 0.58-0.83]). A SAPS-II-cutoff-value of 43.5 seems to be suitable for prognosis of hospital mortality after ICU-CA (specificity: 87.5%, sensitivity: 65.6%; SAPS-II>43.5: 87.5% died in hospital; SAPS-II<43.5: 65.6% survived; odds ratio:13.4 [95% CI: 3.25-54.9]). Also for 1-year-mortality (89.5%; 95% CI: 82.3-94.4) SAPS-II showed a better discrimination between survival and death than SOFA: AUROC: 0.78 (95% CI: 0.65-0.91) vs. 0.69 (95% CI: 0.52-0.87) with a cutoff value of the SAPS-II of 40.5 (specificity: 91.7%, sensitivity: 64.3%; SAPS-II>40.5: 96.4% died; SAPS-II<40.5: 42.3% survived; odd ratio: 19.8 [95% CI: 2.3-168.7]). Compared to SOFA, SAPS-II seems to be more suitable for prediction of hospital and 1-year-mortality after ICU-CA.

Development of a nomogram to predict the prognosis of patients with secondary bone tumors in the intensive care unit: a retrospective analysis based on the MIMIC IV database

PurposeThe present study aimed to develop a nomogram to predict the prognosis of patients with secondary bone tumors in the intensive care unit to facilitate risk stratification and treatment planning.MethodsWe used the MIMIC IV 2.0 (the Medical Information Mart for Intensive Care IV) to retrieve patients with secondary bone tumors as a study cohort. To evaluate the predictive ability of each characteristic on patient mortality, stepwise Cox regression was used to screen variables, and the selected variables were included in the final Cox proportional hazard model. Finally, the performance of the model was tested using the decision curve, calibration curve, and receiver operating characteristic (ROC) curve.ResultsA total of 1028 patients were enrolled after excluding cases with missing information. In the training cohort, albumin, APSIII (Acute Physiology Score III), chemotherapy, lactate, chloride, hepatic metastases, respiratory failure, SAPSII (Simplified Acute Physiology Score II), and total protein were identified as independent risk factors for patient death and then incorporated into the final model. The model showed good and robust prediction performance.ConclusionWe developed a nomogram prognostic model for patients with secondary bone tumors in the intensive care unit, which provides effective survival prediction information.

Prognostic impacts of repeated sepsis in intensive care unit on autoimmune disease patients: a retrospective cohort study

BackgroundAutoimmune diseases (ADs) may be complicated by sepsis when intensive care unit (ICU) admission. But repeated sepsis among AD patients has not been studied yet. The aim of this study is to investigate the impact of repeated in-ICU sepsis on the 1-year overall-cause mortality, septic shock and in-ICU death of AD patients.MethodsData of AD patients with sepsis retrieved from Medical Information Mart for Intensive Care IV (MIMIC-IV) database were divided into the single group and the repeated group according to the frequency of in-ICU sepsis. Propensity score matching was used to balance inter-group bias. Cox proportional hazard regression and sensitivity analysis were utilized to assess the variables on mortality.ResultsThe incidence of repeated in-ICU sepsis in baseline was 19.8%. The repeated in-ICU sepsis was a risk factor for 1-year overall-cause mortality among AD patients (adjusted hazard ratio [HR] = 1.50, 95% CI: 1.16–1.93, P = 0.002), with robust adjusted HRs by the adjustment for confounders in the sensitivity analysis (all P < 0.01). Maximum Sequential Organ Failure Assessment (Max SOFA), Charlson comorbidity index (CCI) and Simplified Acute Physiology Score-II (SAPS-II) were risk factors for 1-year overall-cause mortality among AD with repeated sepsis (Max SOFA: HR = 1.09, P = 0.002; CCI: HR = 1.08, P = 0.039; SAPS-II: HR = 1.03, P < 0.001).ConclusionsCompared to single hit, repeated in-ICU sepsis was independently related to a higher risk of 1-year overall-cause mortality among AD patients. Assessment tools (Higher SOFA, CCI and SAPS-II scores) were closely linked to poor prognosis of AD with repeated sepsis and helped to reflect ill physical conditions for the patients.

Predictive value of white blood cell to hemoglobin ratio for 30-day mortality in patients with severe intracerebral hemorrhage.

To explore the predictive value of white blood cell to hemoglobin ratio (WHR) for 30-day mortality in patients with intracerebral hemorrhage (ICH). In this cohort study, 2,848 patients with ICH were identified in the Medical Information Mart for Intensive Care (MIMIC)-III and MIMIC-IV. Least absolute shrinkage and selection operator (LASSO) regression screened covariates of 30-day mortality of ICH patients. COX regression analysis was used to study the association of different levels of WHR, white blood cell (WBC), and hemoglobin (Hb) with 30-day mortality. The median follow-up time was 30 (20.28, 30.00) days. In total, 2,068 participants survived at the end of the follow-up. WHR was negatively correlated with the Glasgow Coma Score (GCS) (spearman correlation coefficient = -0.143, p < 0.001), and positively associated with the Sepsis-related Organ Failure Assessment (SOFA) score (spearman correlation coefficient = 0.156, p < 0.001), quick SOFA (qSOFA) score (spearman correlation coefficient = 0.156, p < 0.001), and Simplified Acute Physiology Score II (SAPS-II) (spearman correlation coefficient = 0.213, p < 0.001). After adjusting for confounders, WHR >0.833 (HR = 1.64, 95%CI: 1.39-1.92) and WBC >10.9 K/uL (HR = 1.49, 95%CI: 1.28-1.73) were associated with increased risk of 30-day mortality of patients with ICH. The area under the curve (AUC) value of the prediction model based on WHR and other predictors was 0.78 (95%CI: 0.77-0.79), which was higher than SAPSII (AUC = 0.75, 95%CI: 0.74-0.76), SOFA score (AUC = 0.69, 95%CI: 0.68-0.70) and GCS (AUC = 0.59, 95%CI: 0.57-0.60). The level of WHR was associated with 30-day mortality in patients with severe ICH, and the WHR-based prediction model might provide a tool to quickly predict 30-day mortality in patients with ICH.

Development and validation of a nomogram for predicting in-hospital mortality in patients with nonhip femoral fractures

BackgroundThe incidence of nonhip femoral fractures is gradually increasing, but few studies have explored the risk factors for in-hospital death in patients with nonhip femoral fractures in the ICU or developed mortality prediction models. Therefore, we chose to study this specific patient group, hoping to help clinicians improve the prognosis of patients.MethodsThis is a retrospective study based on the data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Least absolute shrinkage and selection operator (LASSO) regression was used to screen risk factors. The receiver operating characteristic (ROC) curve was drawn, and the areas under the curve (AUC), net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the discrimination of the model. The consistency between the actual probability and the predicted probability was assessed by the calibration curve and Hosmer–Lemeshow goodness of fit test (HL test). Decision curve analysis (DCA) was performed, and the nomogram was compared with the scoring system commonly used in clinical practice to evaluate the clinical net benefit.ResultsThe LASSO regression analysis showed that heart rate, temperature, red blood cell distribution width, blood urea nitrogen, Glasgow Coma Scale (GCS), Simplified Acute Physiology Score II (SAPSII), Charlson comorbidity index and cerebrovascular disease were independent risk factors for in-hospital death in patients with nonhip femoral fractures. The AUC, IDI and NRI of our model in the training set and validation set were better than those of the GCS and SAPSII scoring systems. The calibration curve and HL test results showed that our model prediction results were in good agreement with the actual results (P = 0.833 for the HL test of the training set and P = 0.767 for the HL test of the validation set). DCA showed that our model had a better clinical net benefit than the GCS and SAPSII scoring systems.ConclusionIn this study, the independent risk factors for in-hospital death in patients with nonhip femoral fractures were determined, and a prediction model was constructed. The results of this study may help to improve the clinical prognosis of patients with nonhip femoral fractures.

Association between chronic obstructive pulmonary disease and 28-day mortality in patients with sepsis: a retrospective study based on the MIMIC-III database

BackgroundSepsis is a common cause of mortality in critically ill patients, and chronic obstructive pulmonary disease (COPD) is one of the most common comorbidities in septic patients. However, the impact of COPD on patients with sepsis remained unclear. Therefore, the purpose of this study aimed to assess the effect of COPD on the prognosis of septic patients based on Medical Information Mart for Intensive Care (MIMIC-III) database.MethodsIn this retrospective study based on the (MIMIC)-III database version 1.4 (v1.4), we collected clinical data and 28-day all-cause mortality from patients with sepsis in intensive care unit (ICU) and these patients met the diagnostic criteria of Sepsis 3 on ICU admission between 2008 and 2012. International Classification of Diseases (ICD-9) (4660, 490, 4910, 4911, 49120, 49121, 4918, 4919, 4920, 4928, 494, 4940, 4941, 496) was used to identified COPD. We applied Kaplan–Meier analysis to compare difference of 28-day all-cause mortality between septic patients with and without COPD. Cox proportional-hazards model was applied to explore the risk factor associated with 28-day all-cause mortality in patients with sepsis.ResultsSix thousand two hundred fifty seven patients with sepsis were included in this study, including 955 (15.3%) patients with COPD and 5302 patients without COPD (84.7%). Compared with patients without COPD, patients with COPD were older (median: 73.5 [64.4, 82.0] vs 65.8 [52.9, 79.1], P < 0.001), had higher simplified acute physiology score II (SAPSII) (median: 40.0 [33.0, 49.0] vs 38.0 [29.0,47.0], P < 0.001) and greater proportion of mechanical ventilatory support (MV) (55.0% vs 48.9%, P = 0.001). In our study, septic patients with COPD had higher 28-day all-cause mortality (23.6% vs 16.4%, P < 0.001) than patients without COPD. After adjusting for covariates, the results showed that COPD was an independent risk factor for the 28-day all-cause mortality of patients with sepsis (HR 1.30, 95%CI: 1.12–1.50, P = 0.001).ConclusionsCOPD was an independent risk factor of 28-day all-cause mortality in septic patients. Clinically, septic patients with COPD should be given additional care.

The association and prognostic value of systemic inflammatory response index with short and long-term mortality in patients with sepsis.

This study evaluated the association and prognostic significance of the systemic inflammation response index (SIRI) with mortality in sepsis. In this cohort study, the sepsis patients were retrieved from the Medical Information Mart for Intensive Care III (MIMIC-III) and MIMIC-IV intensive care unit (ICU) databases. SIRI was calculated by using the neutrophil, monocyte, and lymphocyte counts. The outcomes were 28-day mortality, 1-year mortality, and 28 days to 1-year mortality. The Cox proportional hazards model with a hazard ratio (HR) and a 95% confidence interval (CI) was used to investigate the association and prognostic value of SIRI with mortality in sepsis. Subgroup analyses of the associations of SIRI with 28-day and 1-year mortality in sepsis were based on age, gender, Simplified Acute Physiology Score II (SAPSII), Sequential Organ Failure Assessment (SOFA), and presence or absence of septic shock. The receiver operating characteristic (ROC) curve was used to compare the predictive performances of SIRI, SOFA and SAPS II for mortality in sepsis. Of the 4239 patients included, 1339 patients suffered from 28-day mortality, 2085 patients suffering from 1-year mortality, and 746 (25.72%) suffered from 28 days to 1-year mortality. High SIRI levels exhibited higher risks of 28-day mortality (HR: 1.15, 95% CI: 1.03-1.29, P = .010), 1-year mortality (HR: 1.14, 95% CI: 1.04-1.24, P = .003), and 28 days to 1-year mortality (HR: 1.16, 95% CI: 1.01-1.35, P = .047) in sepsis. A higher SIRI was reported related to 28-day mortality and 1-year mortality in sepsis patients with female gender, with SOFA < 8, with SAPS II < 44, and in sepsis patients without sepsis shock. The AUC of SIRS, SOFA, and SAPS II in predicting 28-day mortality in sepsis were 0.726, 0.591, and 0.644, respectively. The AUC of SIRI in predicting 1-year mortality in sepsis was 0.761, higher than the AUC values of SOFA and SAPS II. A higher AUC value of SIRI compared with SOFA, and SAPS II in predicting 28 days to 1-year mortality was observed. Elevated SIRI was associated with an increased risk of mortality in sepsis. SIRI is an independent prognostic biomarker of mortality in sepsis.

Risk factors of multidrug-resistant bacteria infection in patients with ventilator-associated pneumonia: A systematic review and meta-analysis

BackgroundMultidrug-resistant (MDR) bacteria-induced VAP often has high lethality. We present this systematic review and meta-analysis to assess the risk factors for MDR bacterial infection in patients with VAP. MethodsPubMed, EMBASE, Web of Science, and Cochrane Library were searched for studies regarding MDR bacterial infection in VAP patients, from Jan 1996 to Aug 2022. Study selection, data extraction, and quality assessment of included studies were conducted by two reviewers independently, and potential risk factors for MDR bacterial infection were identified. ResultsMeta-analysis showed that the score of the Acute Physiology and Chronic Health Evaluation II (APACHE-II) [OR = 1.009, 95% (CI 0.732, 1.287)], Simplified Acute Physiology Score II (SAPS-II) [OR = 2.805, 95%CI (0.854, 4.755)], length of hospital-stay before VAP onset (days) [OR = 2.639, 95%CI (0.387, 4.892)], in-ICU duration [OR = 3.958, 95%CI (0.894, 7.021)], Charlson index [OR = 1.000, 95%CI (0.889, 1.111)], overall hospital-stay [OR = 20.742, 95%CI (18.894, 22.591)], Medication of Quinolones [OR = 2.017, 95%CI (1.339, 3.038)], medication of carbapenems [OR = 3.527, 95%CI (2.476, 5.024)], combination of more than 2 prior antibiotics [OR = 3.181, 95%CI (2.102, 4.812)], and prior use of antibiotics [OR 2.971, 95%CI (2.001, 4.412)] were independent risk factors of MDR bacterial infection in VAP patients. Diabetes and mechanical ventilation duration before VAP onset showed no association with risk for MDR bacterial infection. ConclusionsThis study has identified 10 risk factors associated with MDR bacterial infection in VAP patients. Identification of these factors would be able to facilitate the treatment and prevention of MDR bacterial infection in clinical practice.

Association between BUN/creatinine ratio and the risk of in-hospital mortality in patients with trauma-related acute respiratory distress syndrome: a single-centre retrospective cohort from the MIMIC database

ObjectiveRecent studies have shown that blood urea nitrogen to creatinine (BUN/Cr) ratio might be an effective marker for the prognosis of patients with respiratory diseases. Herein, we aimed to assess...

Simplified acute physiology score II and Mannheim peritonitis index are associated with in-hospital mortality in patients with abdominal sepsis admitted to ICU.

The objective of this study was to assess the association and interaction of laboratory parameters, Simplified Acute Physiology Score II (SAPSII), Modified Shock Index (MSI), and Mannheim Peritonitis Index (MPI) with in-hospital mortality. We conducted a single-center case-control study. Adult patients with abdominal sepsis were included from May 2015 to May 2020. Baseline characteristics, laboratory parameters, SAPSII, MSI, and MPI scores at admission were collected. A principal component (PC) analysis was applied to evaluate variable interactions. In-hospital mortality risk was determined through logistic regression models. One hundred and twenty-seven patients were identified, 60 of which were included for analyses. Non-survivors (48.4%) had a higher frequency of hypertension, lactate and MPI, and lower BE and alactic BE levels. Eight PCs were obtained, PC1 being a linear combination of pH, AG, cAG, alactic BE, bicarbonate, and BE. MPI (OR = 9.87, 95% CI: 3.07-36.61, p = 0.0002), SAPSII (OR = 1.07, 95% CI: 1.01-1.14, p = 0.01), and PC1 (OR = 2.13, 95% CI: 1.12-4.76, p = 0.04) were significantly associated with mortality in univariate analysis, while MPI (OR = 10.1, 95% CI: 3.03-40.06, p = 0.0003) and SAPSII (OR = 1.07, CI95%: 1.01-1.14, p = 0.02) remained significant after adjusting for age and sex. MPI and SAPSII were associated with mortality, although the interaction of laboratory parameters was not.

Construction of a Prediction Model for the Mortality of Elderly Patients with Diabetic Nephropathy.

To construct a prediction model for all-cause mortality in elderly diabetic nephropathy (DN) patients, in this cohort study, the data of 511 DN patients aged ≥65 years were collected and the participants were divided into the training set (n = 358) and the testing set (n = 153). The median survival time of all participants was 2 years. The data in the training set were grouped into the survival group (n = 203) or the death group (n = 155). Variables with P ≤ 0.1 between the two groups were selected as preliminary predictors and involved into the multivariable logistic regression model and the covariables were gradually adjusted. The receiver operator characteristic (ROC), Kolmogorov-Smirnov (KS), and calibration curves were plotted for evaluating the predictive performance of the model. Internal validation of the performance of the model was verified in the testing set. The predictive values of the model were also conducted in terms of people with different genders and ages or accompanied with chronic kidney disease (CKD) or cardiovascular diseases (CVD), respectively. In total, 216 (42.27%) elderly DN patients were dead within 2 years. The prediction model for the 2-year mortality of elderly patients with DN was established based on length of stay (LOS), temperature, heart rate, peripheral oxygen saturation (SpO2), serum creatinine (Scr), red cell distribution width (RDW), the simplified acute physiology score-II (SAPS-II), hyperlipidemia, and the Chronic Kidney Disease Epidemiology Collaboration equation for estimated glomerular filtration rate (eGFR-CKD-EPI). The AUC of the model was 0.78 (95% CI: 0.73–0.83) in the training set and 0.72 (95% CI: 0.63–0.80) in the testing set. The AUC of the model was 0.78 (95% CI: 0.65–0.91) in females and 0.78 (95%CI: 0.68–0.88) in patients ≤75 years. The AUC of the model was 0.74 (95% CI: 0.64–0.84) in patients accompanied with CKD. The model had good predictive value for the mortality of elderly patients with DN within 2 years. In addition, the model showed good predictive values for female DN patients, DN patients ≤75 years, and DN patients accompanied with CKD.

Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage

BackgroundIntracerebral hemorrhage (ICH) is a stroke syndrome with an unfavorable prognosis. Currently, there is no comprehensive clinical indicator for mortality prediction of ICH patients. The purpose of our study was to construct and evaluate a nomogram for predicting the 30-day mortality risk of ICH patients.MethodsICH patients were extracted from the MIMIC-III database according to the ICD-9 code and randomly divided into training and verification cohorts. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression were applied to determine independent risk factors. These risk factors were used to construct a nomogram model for predicting the 30-day mortality risk of ICH patients. The nomogram was verified by the area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).ResultsA total of 890 ICH patients were included in the study. Logistic regression analysis revealed that age (OR = 1.05, P < 0.001), Glasgow Coma Scale score (OR = 0.91, P < 0.001), creatinine (OR = 1.30, P < 0.001), white blood cell count (OR = 1.10, P < 0.001), temperature (OR = 1.73, P < 0.001), glucose (OR = 1.01, P < 0.001), urine output (OR = 1.00, P = 0.020), and bleeding volume (OR = 1.02, P < 0.001) were independent risk factors for 30-day mortality of ICH patients. The calibration curve indicated that the nomogram was well calibrated. When predicting the 30-day mortality risk, the nomogram exhibited good discrimination in the training and validation cohorts (C-index: 0.782 and 0.778, respectively). The AUCs were 0.778, 0.733, and 0.728 for the nomogram, Simplified Acute Physiology Score II (SAPSII), and Oxford Acute Severity of Illness Score (OASIS), respectively, in the validation cohort. The IDI and NRI calculations and DCA analysis revealed that the nomogram model had a greater net benefit than the SAPSII and OASIS scoring systems.ConclusionThis study identified independent risk factors for 30-day mortality of ICH patients and constructed a predictive nomogram model, which may help to improve the prognosis of ICH patients.

A prediction model with measured sentiment scores for the risk of in-hospital mortality in acute pancreatitis: a retrospective cohort study.

BackgroundAccurate and prompt clinical assessment of the severity and prognosis of patients with acute pancreatitis (AP) is critical, particularly during hospitalization. Natural language processing algorithms gain an opportunity from the growing number of free-text notes in electronic health records to mine this unstructured data, e.g., nursing notes, to detect and predict adverse outcomes. However, the predictive value of nursing notes for AP prognosis is unclear. In this study, a predictive model for in-hospital mortality in AP was developed using measured sentiment scores in nursing notes.MethodsThe data of AP patients in the retrospective cohort study were collected from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Sentiments in nursing notes were assessed by sentiment analysis. For each individual clinical note, sentiment polarity and sentiment subjectivity scores were assigned. The in-hospital mortality of AP patients was the outcome. A predictive model was built based on clinical information and sentiment scores, and its performance and clinical value were evaluated using the area under curves (AUCs) and decision-making curves, respectively.ResultsOf the 631 AP patients included, 88 cases (13.9%) cases were dead in hospital. When various confounding factors were adjusted, the mean sentiment polarity was associated with a reduced risk of in-hospital mortality in AP [odds ratio (OR): 0.448; 95% confidence interval (CI): 0.233–0.833; P=0.014]. A predictive model was established in the training group via multivariate logistic regression analysis, including 12 independent variables. In the testing group, the model showed an AUC of 0.812, which was significantly greater than the sequential organ failure assessment (SOFA) of 0.732 and the simplified acute physiology score-II (SAPS-II) of 0.792 (P<0.05). When the same level of risk was considered, the clinical benefits of the predictive model were found to be the highest compared with SOFA and SAPS-II scores.ConclusionsThe model combined sentiment scores in nursing notes showed well predictive performance and clinical value in in-hospital mortality of AP patients.

Systemic Inflammation Evaluated by Interleukin-6 or C-Reactive Protein in Critically Ill Patients: Results From the FROG-ICU Study.

BackgroundThe prognostic impact of high concentration of interleukin-6 (IL-6) or C-reactive protein (CRP), two routinely available markers of systemic inflammation in the general population of critically ill patients, remains unclear. In a large cohort of critically ill patients including septic and non-septic patients, we assessed the relationship between baseline IL-6 or CRP and mortality, organ dysfunction, and the need for organ support.MethodsThis was an ancillary analysis of the prospective French and euRopean Outcome reGistry in Intensive Care Units (FROG-ICU) study including patients with a requirement for invasive mechanical ventilation and/or vasoactive drug support for more than 24 h following intensive care unit (ICU) admission. The primary objective was to determine the association between baseline IL-6 or CRP concentration and survival until day 90. Secondary outcomes included organ dysfunction as evaluated by the Sequential Organ Failure Assessment (SOFA) score, and the need for organ support, including vasopressors/inotropes and/or renal replacement therapy (RRT).ResultsMedian IL-6 and CRP concentrations (n = 2,076) at baseline were 100.9 pg/ml (IQR 43.5–261.7) and 143.7 mg/L (IQR 78.6–219.8), respectively. Day-90 mortality was 30%. High IL-6 or CRP was associated with worse 90-day survival (hazard ratios 1.92 [1.63–2.26] and 1.21 [1.03–1.41], respectively), after adjustment on the Simplified Acute Physiology Score II (SAPS-II). High IL-6 was also associated with the need for organ-support therapies, such as vasopressors/inotropes (OR 2.67 [2.15–3.31]) and RRT (OR 1.55 [1.26–1.91]), including when considering only patients independent from those supports at the time of IL-6 measurement. Associations between high CRP and organ support were inconsistent.ConclusionIL-6 appears to be preferred over CRP to evaluate critically ill patients’ prognoses.

Alcohol Abuse Associated With Increased Risk of Angiographic Vasospasm and Delayed Cerebral Ischemia in Patients With Aneurysmal Subarachnoid Hemorrhage Requiring Mechanical Ventilation.

ObjectiveAlthough alcohol abuse has been indicated to cause cerebral aneurysm development and rupture, there is limited data on the impact of alcohol abuse on outcomes after an aneurysmal subarachnoid hemorrhage (aSAH). This study aims to investigate whether alcohol abuse increases the risk of angiographic vasospasm and delayed cerebral ischemia (DCI) in critically ill patients with aSAH.MethodsWe conducted a secondary analysis based on a retrospective study in a French university hospital intensive care unit (ICU). Patients with aSAH requiring mechanical ventilation hospitalized between 2010 and 2015 were included. Patients were segregated according to alcohol abuse (yes or no). Multivariable logistic regression analysis was used to identify the independent risk factors associated with angiographic vasospasm and DCI.ResultsThe patient proportion of alcohol abuse was dramatically greater in males than that in females (p < 0.001). The Simplified Acute Physiology Score II (SAPSII) score on admission did not show a statistical difference. Neither did the World Federation of Neurosurgical Societies (WFNS) and Fisher scores. Patients with alcohol abuse were more likely to develop angiographic vasospasm (OR 3.65, 95% CI 1.17–11.39; p = 0.0260) and DCI (OR 3.53, 95% CI 1.13–10.97; p = 0.0294) as evidenced by multivariable logistic regression analysis.ConclusionsIn this study, patients with alcohol abuse are at higher odds of angiographic vasospasm and DCI, which are related to poor prognosis following aSAH. These findings are important for the prevention and clinical management of aSAH.

Machine Learning for Prediction of Outcomes in Cardiogenic Shock.

ObjectiveThe management of cardiogenic shock (CS) in the elderly remains a major clinical challenge. Existing clinical prediction models have not performed well in assessing the prognosis of elderly patients with CS. This study aims to build a predictive model, which could better predict the 30-day mortality of elderly patients with CS.MethodsWe extracted data from the Medical Information Mart for Intensive Care III version 1.4 (MIMIC-III) as the training set and the data of validation sets were collected from the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University. Three models, including the cox regression model, the Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and the CoxBoost model, were established using the training set. Through the comparison of area under the receiver operating characteristic (ROC) curve (AUC), C index, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and median improvement in risk score, the best model was selected. Then for external validation, compared the best model with the simplified acute physiology score II (SAPSII) and the CardShock risk score.ResultsA total of 919 patients were included in the study, of which 804 patients were in the training set and 115 patients were in the verification set. Using the training set, we built three models: the cox regression model including 6 predictors, the LASSO regression model including 4 predictors, and the CoxBoost model including 16 predictors. Among them, the CoxBoost model had good discrimination [AUC: 0.730; C index: 0.6958 (0.6657, 0.7259)]. Compared with the CoxBoost model, the NRI, IDI, and median improvement in risk score of other models were all<0. In the validation set, the CoxBoost model was also well-discriminated [AUC: 0.770; C index: 0.7713 (0.6751, 0.8675)]. Compared with the CoxBoost model, the NRI, IDI, and median improvement in risk score of SAPS II and the CardShock risk score were all < 0. And we constructed a dynamic nomogram to visually display the model.ConclusionIn conclusion, this study showed that in predicting the 30-day mortality of elderly CS patients, the CoxBoost model was superior to the Cox regression model, LASSO regression model, SAPS II, and the CardShock risk score.

Comparison of elevated cardiac troponin I with SAPS-II and APACHE-II score in predicting outcome of severe intoxications.

Background and Aims:To date, different methods have been invented to risk-stratify critically ill patients, however, there is a paucity of information regarding assessing the severity of poisonings. This study was designed to determine the comparative efficacy of Simplified Acute Physiology Score-II (SAPS-II) and Acute Physiology and Chronic Health Evaluation-II (APACHE-II)score with cardiac troponin I (cTnI) in predicting severe intoxication outcomes.Methods:This was a prospective study conducted on patients who fulfilled defined severe intoxication criteria necessitating intensive care unit (ICU) admission over a period of 6 months. SAPS-II and APACHE-II scores were calculated and cTnI concentrations were measured. These indicators were compared to determine which has the better ability to prognosticate mortality and complications.Results:A total of 55 cases (median age, 35 [24-49] years) were enroled. Eight patients (14.5%) died. Mean SAPS-II, median APACHE-II score and median cTnI concentrations were 32.05 ± 11.24, 13 [10-17] and 0.008 [0.002-0.300] ng/ml, respectively, which were significantly different between the survivors and non-survivors. Receiver operating characteristics curve results of SAPS-II, APACHE-II score and cTnI concentrations in predicting mortality were 0.945, 0.932 and 0.763 and in predicting complications were 0.779, 0.739 and 0.727, respectively. High cTnI concentration (>0.37 ng/ml) correlated with soft clinical outcomes, including length of ventilatory support, length of ICU stay and length of hospital stay (LOS) (r: 0.928, 0.881 and 0.735 respectively; all P < 0.001).Conclusion:SAPS-II scores were superior in predicting death and complications, while cTnI correlated more closely with soft clinical outcomes, such as the length of ventilator support, length of ICU stay or LOS.

Risk factor analysis and nomogram for predicting in-hospital mortality in ICU patients with sepsis and lung infection

BackgroundLung infection is a common cause of sepsis, and patients with sepsis and lung infection are more ill and have a higher mortality rate than sepsis patients without lung infection. We constructed a nomogram prediction model to accurately evaluate the prognosis of and provide treatment advice for patients with sepsis and lung infection.MethodsData were retrospectively extracted from the Medical Information Mart for Intensive Care (MIMIC-III) open-source clinical database. The definition of Sepsis 3.0 [10] was used, which includes patients with life-threatening organ dysfunction caused by an uncontrolled host response to infection, and SOFA score ≥ 2. The nomogram prediction model was constructed from the training set using logistic regression analysis, and was then internally validated and underwent sensitivity analysis.ResultsThe risk factors of age, lactate, temperature, oxygenation index, BUN, lactate, Glasgow Coma Score (GCS), liver disease, cancer, organ transplantation, Troponin T(TnT), neutrophil-to-lymphocyte ratio (NLR), and CRRT, MV, and vasopressor use were included in the nomogram. We compared our nomogram with the Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiology Score II (SAPSII), the nomogram had better discrimination ability, with areas under the receiver operating characteristic curve (AUROC) of 0.743 (95% C.I.: 0.713–0.773) and 0.746 (95% C.I.: 0.699–0.790) in the training and validation sets, respectively. The calibration plot indicated that the nomogram was adequate for predicting the in-hospital mortality risk in both sets. The decision-curve analysis (DCA) of the nomogram revealed that it provided net benefits for clinical use over using the SOFA score and SAPSII in both sets.ConclusionOur new nomogram is a convenient tool for accurate predictions of in-hospital mortality among ICU patients with sepsis and lung infection. Treatment strategies that improve the factors considered relevant in the model could increase in-hospital survival for these ICU patients.