The Comparison of scoring systems: SOFA, APACHE-II, LODS, MODS, and SAPS-II in critically ill elderly sepsis patients.

INTRODUCTION: The elderly population is unique and the prognostic scoring systems developed for the adult population need to be validated. We evaluated the predictive value of frequently used scoring systems on mortality in critically ill elderly sepsis patients. METHODOLOGY: In this single-center, observational, prospective study, critically ill elderly sepsis patients were evaluated. Sequential organ failure evaluation score (SOFA), acute physiology and chronic health evaluation score-II (APACHE-II), logistic organ dysfunction score (LODS), multiple organ dysfunction score (MODS), and simplified acute physiology score-II (SAPS-II) were calculated. The participants were followed up for 28 days for in-hospital mortality. Prognostic scoring systems, demographic characteristics, comorbid conditions, and baseline laboratory findings were compared between "survivor" and "non-survivor" groups. RESULTS: 202 patients with a mean age of 79 (interquartile range, IQR: 11) years were included, and 51% (n = 103) were female. The overall mortality was 41% (n = 83). SOFA, APACHE-II, LODS, MODS, and SAPS-II scores were significantly higher in the non-survivor group (p < 0.001), and higher scores were correlated with higher mortality. The receiver operator characteristics (ROC) - area under curve (AUC) values were 0.802, 0.784, 0.735, 0.702 and 0.780 for SOFA, APACHE-II, LODS, MODS, and SAPS-II, respectively. All prognostic scoring models had a significant discriminative ability on the prediction of mortality among critically ill elderly sepsis patients (p < 0.001). CONCLUSIONS: This study showed that SOFA, APACHE-II, LODS, MODS, and SAPS-II scores are significantly associated with 28-day mortality in critically ill elderly sepsis patients, and can be successfully used for predicting mortality.

A first-level customization study of SAPS II with Norwegian Intensive Care and Pandemic Registry (NIPaR) data.

BACKGROUND: Severity scores and mortality prediction models (MPMs) are important tools for benchmarking and stratification in the intensive care unit (ICU) and need to be regularly updated using data from a local and contextual cohort. Simplified acute physiology score II (SAPS II) is widely used in European ICUs. METHODS: A first-level customization was performed on the SAPS II model using data from the Norwegian Intensive Care and Pandemic Registry (NIPaR). Two previous SAPS II models (Model A: the original SAPS II model and Model B: a SAPS II model based on NIPaR data from 2008 to 2010) were compared to the new Model C. Model C was based on patients from 2018 to 2020 (corona virus disease 2019 patients omitted; n = 43,891), and its performances (calibration, discrimination, and uniformity of fit) compared to the previous models (Model A and Model B). RESULTS: Model C was better calibrated than Model A with a Brier score 0.132 (95% confidence interval 0.130-0.135) versus 0.143 (95% confidence interval 0.141-0.146). The Brier score for Model B was 0.133 (95% confidence interval 0.130-0.135). In the Cox's calibration regression α ≈ 0 and ß ≈ 1 for both Model C and Model B but not for Model A. Uniformity of fit was similar for Model B and for Model C, both better than for Model A, across age groups, sex, length of stay, type of admission, hospital category, and days on respirator. The area under the receiver operating characteristic curve was 0.79 (95% confidence interval 0.79-0.80), showing acceptable discrimination. CONCLUSIONS: The observed mortality and corresponding SAPS II scores have significantly changed during the last decades and an updated MPM is superior to the original SAPS II. However, proper external validation is required to confirm our findings. Prediction models need to be regularly customized using local datasets in order to optimize their performances.

Predictive outcomes of APACHE II and expanded SAPS II mortality scoring systems in coronary care unit.

OBJECTIVE: We investigated the predictive values of the expanded Simplified Acute Physiology Score (SAPS) II and Acute Physiologic Score and Chronic Health Evaluation (APACHE) II score in predicting in-hospital mortality in coronary care unit (CCU) patients. METHODS: In this study, expanded SAPS II and APACHE II scores were calculated in the CCU of a single-center tertiary hospital. Patients admitted to CCU with any cardivascular indication were included in the study. Both scores were calculated according to previously determined criteria. Calibration and discrimination abilities of the scores in predicting in-hospital mortality were tested with Hosmer-Lemeshow goodness-of-fit C chi-square and receiver operating characteristics (ROC) curve analyses. RESULTS: A total of 871 patients were included in the analysis. The goodness-of-fit C chi-square test showed that both scores have a good performance in predicting survivors and nonsurvivors in CCU. Expanded SAPS II score has a sensitivity of 80% and a specificity of 91.8% with the cut-off value of 5.55, while APACHE II has a sensitivity of 75.9% and a specificity of 87.4% with the cut-off value of 16.5 in predicting mortality. CONCLUSION: Expanded SAPS II and APACHE II scores have good ability to predict in-hospital mortality in CCU patients. Therefore, they can be used as a tool to predict short-term mortality in cardiovascular emergencies.

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

OBJECTIVE: 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. MATERIAL AND METHODS: 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. RESULTS: 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. CONCLUSION: MPI and SAPSII were associated with mortality, although the interaction of laboratory parameters was not.

OBJETIVO: Evaluar la asociación e interacción de los parámetros de laboratorio, SAPSII, MSI y MPI con la mortalidad intrahospitalaria. MATERIALES Y MÉTODOS: Nosotros realizamos un estudio de casos y controles de pacientes adultos con sepsis abdominal desde mayo 2015 a mayo 2020. Recolectamos las características basales, parámetros de laboratorio, SAPSII, MSI y MPI al ingreso. Se aplicó un Análisis de Componentes Principales. El riesgo de mortalidad intrahospitalaria se determinó mediante modelos de regresión logística. RESULTADOS: Identificamos 127 pacientes, 60 de los cuales se incluyeron. Los no supervivientes (48,4%) tuvieron mayor frecuencia de HAS, lactato y MPI, y menores niveles de EB y EB aláctico. Se obtuvieron ocho Componentes Principales (PC), siendo PC1 una combinación lineal de pH, AG, cAG, EB aláctico, bicarbonato y EB. MPI (OR = 9.87, IC95%: 3.07-36.61, p = 0.0002), SAPSII (OR = 1.07, IC95%: 1.01-1.14, p = 0.01) y PC1 (OR = 2.13, IC95%: 1.12-4.76, p = 0.04) se asociaron significativamente con la mortalidad en el análisis univariado, mientras que MPI (OR = 10.1, IC95%: 3.03-40.06, p = 0.0003) y SAPSII (OR = 1.07, IC 95%: 1.01-1.14, p = 0.02) permanecieron significativos después del ajuste por edad y sexo. CONCLUSIONES: MPI y SAPSII se asociaron con mortalidad, aunque la interacción de los parámetros de laboratorio no lo hizo.

Optimized diagnosis-based comorbidity measures for all-cause mortality prediction in a national population-based ICU population.

BACKGROUND: We aimed to optimize prediction of long-term all-cause mortality of intensive care unit (ICU) patients, using quantitative register-based comorbidity information assessed from hospital discharge diagnoses prior to intensive care treatment. MATERIAL AND METHODS: Adult ICU admissions during 2006 to 2012 in the Swedish intensive care register were followed for at least 4 years. The performance of quantitative comorbidity measures based on the 5-year history of number of hospital admissions, length of stay, and time since latest admission in 36 comorbidity categories was compared in time-to-event analyses with the Charlson comorbidity index (CCI) and the Simplified Acute Physiology Score (SAPS3). RESULTS: During a 7-year period, there were 230,056 ICU admissions and 62,225 deaths among 188,965 unique individuals. The time interval from the most recent hospital stays and total length of stay within each comorbidity category optimized mortality prediction and provided clear separation of risk categories also within strata of age and CCI, with hazard ratios (HRs) comparing lowest to highest quartile ranging from 1.17 (95% CI: 0.52-2.64) to 6.41 (95% CI: 5.19-7.92). Risk separation was also observed within SAPS deciles with HR ranging from 1.07 (95% CI: 0.83-1.38) to 3.58 (95% CI: 2.12-6.03). CONCLUSION: Baseline comorbidity measures that included the time interval from the most recent hospital stay in 36 different comorbidity categories substantially improved long-term mortality prediction after ICU admission compared to the Charlson index and the SAPS score. Trial registration ClinicalTrials.gov ID NCT04109001, date of registration 2019-09-26 retrospectively.

Comparison of POSSUM and SAPS II in Prediction of Postoperative Mortality in Hollow Viscus Perforation.

BACKGROUND: There have been very few studies in the literature assessing various scoring systems to predict mortality in patients with hollow viscous perforation. Scoring systems like POSSUM and SAPS II are among the most widely validated risk predictors. Objective of the study was to compare POSSUM and SAPS II in prediction of mortality in patients undergoing surgery for hollow viscus perforation. METHODS: Prospective observational study was conducted at Department of Surgery, Tribhuvan University Teaching Hospital, Kathmandu, Nepal, over a period of 18 months. Ethical approval was obtained from the Institutional Review Board of Institute of Medicine. Informed consent was taken from all the patients. Patients aged less than 16 years, discharged on request and patients in whom no perforation found during surgery were excluded from the study. RESULTS: Among 121 patients enrolled in the study, in-hospital mortality was seen in 19 patients (17.0%). Mean POSSUM score in survivors was 39.7 ± 7.3 and in non-survivors was 52.8 ± 5.8 (p < 0.001). Similarly mean SAPS II score was 16.4 ± 9.7 in survivors and 41.8 ± 6.4 in non-survivors ( p < 0.001). Area under ROC curve was higher for SAPS II (0.964) as compared to POSSUM (0.906) suggesting that SAPS was better. CONCLUSIONS: Both POSSUM and SAPS II provided good discrimination between survivors and non survivors in patients undergoing surgery for hollow viscus perforation. SAPS II showed better sensitivity and specificity than POSSUM in predicting mortality.

Performance of NUTRIC score to predict 28-day mortality in critically ill patients after replacing APACHE II with SAPS 3.

OBJECTIVES: The Nutrition Risk in the Critically Ill (NUTRIC) score has been advocated as a screening tool for nutrition risk assessment in critically ill patients. It was developed and validated to predict 28-day mortality using Acute Physiology and Chronic Health Evaluation II (APACHE II) score as one of its components. However, nowadays the Simplified Acute Physiology Score 3 (SAPS 3) demonstrates better performance. We aimed to test the performance of NUTRIC score in predicting 28-day mortality after replacement of APACHE II by SAPS 3, and the interaction between nutrition adequacy and mortality. METHODS: Adult patients who received nutrition therapy and remained >3 days in intensive care unit were retrospectively evaluated. In order to replace APACHE II component, we used ranges of SAPS 3 with similar predicted mortality. Discrimination between these tools in predicting 28-day mortality was assessed using the ROC curve, calibration was evaluated with calibration belt, and correlation with intraclass correlation. The relationship between nutritional adequacy and mortality was assessed in a subgroup with available data. RESULTS: 542 patients were analyzed (median age of 78 years old, 73.4% admitted for non-surgical reasons and 28-day mortality was 18.1%). Mortality prediction discrimination did not differ between tools (p>0.05), but showed a good agreement (intraclass correlation 0.86) with good calibration. In the subgroup analysis for nutritional adequacy (n = 99), no association with mortality was observed. CONCLUSION: Performance of NUTRIC score with SAPS 3 is similar to the original tool. Therefore, it might be used in settings where APACHE II is not available.

Predict models for prolonged ICU stay using APACHE II, APACHE III and SAPS II scores: A Japanese multicenter retrospective cohort study.

Prolonged ICU stays are associated with high costs and increased mortality. Thus, early prediction of such stays would help clinicians to plan initial interventions, which could lead to efficient utilization of ICU resources. The aim of this study was to develop models for predicting prolonged stays in Japanese ICUs using APACHE II, APACHE III and SAPS II scores. In this multicenter retrospective cohort study, we analyzed the cases of 85,558 patients registered in the Japanese Intensive care Patient Database between 2015 and 2019. Prolonged ICU stay was defined as an ICU stay of >14 days. Multivariable logistic regression analyses were performed to develop three predictive models for prolonged ICU stay using APACHE II, APACHE III and SAPS II scores, respectively. After exclusions, 79,620 patients were analyzed, 2,364 of whom (2.97%) experienced prolonged ICU stays. Multivariable logistic regression analyses showed that severity scores, BMI, MET/RRT, postresuscitation, readmission, length of stay before ICU admission, and diagnosis at ICU admission were significantly associated with higher risk of prolonged ICU stay in all models. The present study developed predictive models for prolonged ICU stay using severity scores. These models may be helpful for efficient utilization of ICU resources.

[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].

OBJECTIVE: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

Critically ill COVID-19 patients in northeast Brazil: mortality predictors during the first and second waves including SAPS 3.

BACKGROUND: The Simplified Acute Physiology Score (SAPS) 3 is a reliable score to predict mortality. This study aims to investigate the predictive values of SAPS 3 and other clinical parameters for death in critically ill coronavirus disease 2019 (COVID-19) patients. METHODS: This is a prospective study in a tertiary hospital for patients who required intensive care due to COVID-19 infection in northeast Brazil. Two distinct groups were constructed according to the epidemiological data: first wave and second wave. The severity of patients admitted was estimated using the SAPS 3 score. RESULTS: A total of 767 patients were included: 290 were enrolled in the first wave and 477 in the second wave. Patients in the first wave had more comorbidities, were put on mechanical ventilation and required dialysis and vasopressors more frequently (p<0.05). During the second wave, non-invasive ventilation was more often required (p<0.05). In both periods, older patients and higher SAPS 3 scores on admission were associated with death (p<0.05). Non-invasive ventilation use showed a negative association with death only in the second wave period. In the first wave, the SAPS 3 score was more useful (area under the curve [AUC] 0.897) in predicting death in critically ill COVID-19 patients than in the second wave (AUC 0.810). CONCLUSION: The SAPS 3 showed very reliable predictive values for death during the waves of the COVID-19 pandemic, mostly together with kidney and pulmonary dysfunction.

Simplified Acute Physiology Score 3 Performance in Austrian COVID-19 Patients Admitted to Intensive Care Units with and without Diabetes.

This study evaluated and compared the performance of simplified acute physiology score 3 (SAPS 3) for predicting in-hospital mortality in COVID-19 patients admitted to intensive care units (ICUs) with and without diabetes in Austria. The Austrian national public health institute (GÖG) data of COVID-19 patients admitted to ICUs (n = 5850) were analyzed. Three versions of SAPS 3 were used: standard equation, Central European equation, and Austrian equation customized for COVID-19 patients. The observed in-hospital mortality was 38.9%, 42.9%, and 37.3% in all, diabetes, and non-diabetes patients, respectively. The overall C-statistics was 0.69 with an insignificant (p = 0.193) difference between diabetes (0.70) and non-diabetes (0.68) patients. The Brier score was > 0.20 for all SAPS 3 equations in all cohorts. Calibration was unsatisfactory for both standard and Central European equations in all cohorts, whereas it was satisfactory for the Austrian equation in diabetes patients only. The SAPS 3 score demonstrated low discrimination and accuracy in Austrian COVID-19 patients, with an insignificant difference between diabetes and non-diabetes. All equations were miscalibrated particularly in non-diabetes patients, while the Austrian equation showed satisfactory calibration in diabetes patients only. Both uncalibrated and calibrated versions of SAPS 3 should be used with caution in COVID-19 patients.

Development and validation of a prediction model for in-hospital mortality of patients with severe thrombocytopenia.

Risk stratification and prognosis evaluation of severe thrombocytopenia are essential for clinical treatment and management. Currently, there is currently no reliable predictive model to identify patients at high risk of severe thrombocytopenia. This study aimed to develop and validate a prognostic nomogram model to predict in-hospital mortality in patients with severe thrombocytopenia in the intensive care unit. Patients diagnosed with severe thrombocytopenia (N = 1561) in the Medical Information Mart for Intensive Care IV database were randomly divided into training (70%) and validation (30%) cohorts. In the training cohort, univariate and multivariate logistic regression analyses with positive stepwise selection were performed to screen the candidate variables, and variables with p < 0.05 were included in the nomogram model. The nomogram model was compared with traditional severity assessment tools and included the following 13 variables: age, cerebrovascular disease, malignant cancer, oxygen saturation, heart rate, mean arterial pressure, respiration rate, mechanical ventilation, vasopressor, continuous renal replacement therapy, prothrombin time, partial thromboplastin time, and blood urea nitrogen. The nomogram was well-calibrated. According to the area under the receiver operating characteristics, reclassification improvement, and integrated discrimination improvement, the nomogram model performed better than the traditional sequential organ failure assessment (SOFA) score and simplified acute physiology score II (SAPS II). Additionally, according to decision curve analysis, a threshold probability between 0.1 and 0.75 indicated that our constructed nomogram model showed more net benefits than the SOFA score and SAPS II. The nomogram model we established showed superior predictive performance and can assist in the quantitative assessment of the prognostic risk in patients with severe thrombocytopenia.

Comparative Study of Sofa, Apache Ii, Saps Ii, as a Predictor of Mortality in Patients of Sepsis Admitted in Medical ICU.

Sepsis is a life-threatening organ dysfunction with high mortality and morbidity. Various mortality prediction scores are currently in use for prediction of mortality. Although combination of various scores have not been used before. The aim of the study was to compare SOFA, APACHE II, SAPS II, as a predictor of mortality and to assess the usefulness of combination of different scores. MATERIAL: A one-year hospital based prospective study conducted from 1st January 2020 to 31st December 2020 in medical ICU, where 100 patients of sepsis admitted in ICU with evidence of organ dysfunction were included in the study and various scores like SOFA, APACHE II, and SAPS II were calculated at 24 and 48 hours of admission, using laboratory results and clinical examination. and an attempt to access for predictive accuracy of combination of scores was undertaken. OBSERVATION: Majority of the patients (37%) were in the age group of 60-79 years with maximum mortality in this age group of (39.22 %). Mortality rate was 51%, with higher mortality in the female group being 68.63%. Diabetes was most common comorbid in our study (41%). No significant difference was observed in physiological variable over 24 and 48 hours, however decrease in WBC and platelet count was noted at the end of 48 hours; Mean SOFA, APACHE II, SAPS II were significantly higher in the mortality group than the recovery group; All three scores had good diagnostic performance, with max sensitivity at 24 and 48 hours with APACHE II being 64.10% and 78.79% respectively, max specificity at 24 and 48 hours was noticed with SAPS II being 96.97% and 87.88% respectively. On further combination of scores, maximum sensitivity was seen with SOFA plus APACHE II at 48 hours of 74.36%, maximum specificity was seen at 24 hours with SOFA plus SAPS II of 93.94%. Upon application of Youden's index to the combination of scores, best diagnostic performance was seen with SOFA plus SAPS II at 48 hours. CONCLUSION: All the three scores showed good mortality prediction rate but among the scores higher sensitivity was seen with APACHE II score at 24 and 48 hours and higher specificity was seen with SAPS II at 24 and 48 hours. Combination of scores did show a slightly better predictability with combination of SAPS II and SOFA showing maximum Youden's index at 48 hours. Mortality was comparatively higher among the females and elderly group with most common risk factor being diabetes.

The predictive value of the Oxford Acute Severity of Illness Score for clinical outcomes in patients with acute kidney injury.

OBJECTIVE: To compare the performance of the Oxford Acute Severity of Illness Score (OASIS), the Acute Physiology and Chronic Health Evaluation II (APACHE II) score, the Simplified Acute Physiology Score II (SAPS II), and the Sequential Organ Failure Assessment (SOFA) score in predicting 28-day mortality in acute kidney injury (AKI) patients. METHODS: Data were extracted from the Beijing Acute Kidney Injury Trial (BAKIT). A total of 2954 patients with complete clinical data were included in this study. Receiver operating characteristic (ROC) curves were used to analyze and evaluate the predictive effects of the four scoring systems on the 28-day mortality risk of AKI patients and each subgroup. The best cutoff value was identified by the highest combined sensitivity and specificity using Youden's index. RESULTS: Among the four scoring systems, the area under the curve (AUC) of OASIS was the highest. The comparison of AUC values of different scoring systems showed that there were no significant differences among OASIS, APACHE II, and SAPS II, which were better than SOFA. Moreover, logistic analysis revealed that OASIS was an independent risk factor for 28-day mortality in AKI patients. OASIS also had good predictive ability for the 28-day mortality of each subgroup of AKI patients. CONCLUSION: OASIS, APACHE II, and SAPS II all presented good discrimination and calibration in predicting the 28-day mortality risk of AKI patients. OASIS, APACHE II, and SAPS II had better predictive accuracy than SOFA, but due to the complexity of APACHE II and SAPS II calculations, OASIS is a good substitute. TRIAL REGISTRATION: This study was registered at www.chictr.org.cn (registration number Chi CTR-ONC-11001875). Registered on 14 December 2011.

Superiority of Simplified Acute Physiologic Score II Compared with Acute Physiologic and Chronic Health Evaluation II and Sequential Organ Failure Assessment Scores for Predicting 48-Hour Mortality in Patients Receiving Continuous Kidney Replacement Therapy.

BACKGROUND: Predicting early mortality is important in patients undergoing continuous kidney replacement therapy (CKRT), especially in the first 48 h. This study aimed to determine the predictive performance of the Simplified Acute Physiologic Score (SAPS) II, the Acute Physiologic and Chronic Health Evaluation (APACHE) II, and the Sequential Organ Failure Assessment (SOFA) scores for early mortality in patients receiving CKRT. METHODS: Data from patients with acute kidney injury receiving CKRT were consecutively and retrospectively obtained at a tertiary medical center between August 2017 and March 2021. The outcomes included 48-h and 7-day mortality. The scoring systems were evaluated via discrimination at the time of CKRT initiation (using area under the receiver operating characteristics curve [AUROC]) and calibration (via Hosmer-Lemeshow goodness-of-fit C statistics). RESULTS: Among eligible 652 patients, 95 (14.6%) and 212 (32.5%) died within 48 h and within 7 days, respectively. The AUROC for SAPS II (0.71, 95% confidence interval [CI]: 0.65-0.77, p = 0.016 vs. APACHE II score, p = 0.044 vs. SOFA score) was significantly higher than that of the APACHE II (0.66, 95% CI: 0.60-0.72) and SOFA scores (0.66, 95% CI: 0.60-0.72) for 48-h mortality. However, no significant differences in the AUROCs for SAPS II, APACHE II, and SOFA scores for 7-day mortality were observed. The calibration of the SAPS II for 48-h and 7-day mortality was adequate (p = 0.507 and p = 0.141, respectively). CONCLUSIONS: The predictive performance of SAPS II for mortality within the first 48 h was superior to that of the APACHE II and SOFA scores in patients with acute kidney injury receiving CKRT.

SAPS III is superior to SOFA for predicting 28-day mortality in sepsis patients based on Sepsis 3.0 criteria.

INTRODUCTION: The discrimination and calibration accuracy of prediction models tends to become poor over time. The performance of predictive models should be reevaluated periodically. The aim of this study was to reassess the discrimination of the six commonly used models for predicting 28-day mortality in patients with sepsis based on the Sepsis 3.0 criteria. METHODS: Patient data were extracted from the fourth edition of the Medical Information Mart for Critical Care (MIMIC IV) database. The systemic inflammatory response syndrome (SIRS), Sequential Organ Failure Assessment (SOFA), Oxford Acute Severity of Illness Score (OASIS), Logistic Organ Dysfunction System (LODS), and Simplified Acute Physiology Score II (SAPS II) and III (SAPS III) scores were calculated and collected. The area under the receiver operating characteristic curve (AUROC) was used to compare the discrimination abilities of the models using non-parametric Wilcoxon statistics. The Delong method was used to perform pairwise comparisons of the AUROCs of the models. Multiple subgroup analyses for age, body mass index, and sex were performed with regard to the 28-day mortality prediction of the models. RESULTS: A total of 12 691 patients were included. The mean age of the patients was 65.97 ± 15.77 years; 7673 patients (60.50%) were male. The mean SIRS, SOFA, OASIS, SAPS II, LODS, and SAPS III scores were higher in the non-survivor group than in the survivor group. The discrimination for 28-day mortality with the SAPS III (AUROC 0.812, 95% confidence interval (CI) 0.802-0.822) and LODS (AUROC 0.804, 95% CI 0.743-0.765) models was superior to that of the SIRS (AUROC 0.575, 95% CI 0.562-0.589), SOFA (AUROC 0.612, 95% CI 0.598-0.626), OASIS (AUROC 0.753, 95% CI 0.742-0.764), and SAPS II (AUROC 0.754, 95% CI 0.743-0.765) models. The Youden index of the SAPS III model was 0.484, which was the highest among the models. Subgroup analyses showed similar results to the overall results. CONCLUSIONS: The discrimination for 28-day mortality with the SAPS III and LODS models was superior to that of the SIRS, SOFA, OASIS, and SAPS II models. The SAPS III model showed the best discrimination capacity for 28-day mortality compared with the other models.

Predicting mortality for critically ill burns patients, using the Abbreviated Burn Severity Index and Simplified Acute Physiology Score 3.

INTRODUCTION: Reviewing the profile of patients admitted at the Burns Intensive Care Unit at São Paulo Hospital - UNIFESP, as well as the available literature, it becomes evident the need for tools able to predict those patients' outcomes. Distinct score models are used in different health centers, not only as prognostic models, but also as research and quality control tools. Amongst these prognostic scores, there are two strands, the burns specific scores - which consider the injury's characteristics - and the general critical patient's scores. OBJECTIVE: This study aims to analyze the differences and tendencies in mortality prediction of two broadly used scores when applied to São Paulo Hospital's Burns Intensive Care Unit patients, ABSI - burns specific score - and SAPS 3 - general score for critical patients. METHODS: This is an individual, observational, retrospective and comparative study, developed with medical records review. Both scores were applied to every patient admitted at São Paulo Hospital's Burns Intensive Care Unit from 2011 to 2016. Statistical analyses used the non-parametric test of Kolmogorov-Smirnov, a p-value <0.05 was considered significant. RESULTS: 122 patients were included, the average age was 34,4 years old. 70,5% of patients were male and 49% had a total body surface area burned of 20%. 27% of the patients died. Statistical analyses do not show significant differences between ABSI and SAPS3 mortality predictions for burns patients at this health center. CONCLUSION: The study evidences that SAPS 3 score, frequently used at general Intensive Care Units, has a similar performance to ABSI score, which is specific for burns populations. ABSI score is easier to implement, as it is simpler and able to show instant results.

Mortality prediction in intensive care units including premorbid functional status improved performance and internal validity.

OBJECTIVE: Prognostic models are key for benchmarking intensive care units (ICUs). They require up-to-date predictors and should report transportability properties for reliable predictions. We developed and validated an in-hospital mortality risk prediction model to facilitate benchmarking, quality assurance, and health economics evaluation. STUDY DESIGN AND SETTING: We retrieved data from the database of an international (Finland, Estonia, Switzerland) multicenter ICU cohort study from 2015 to 2017. We used a hierarchical logistic regression model that included age, a modified Simplified Acute Physiology Score-II, admission type, premorbid functional status, and diagnosis as grouping variable. We used pooled and meta-analytic cross-validation approaches to assess temporal and geographical transportability. RESULTS: We included 61,224 patients treated in the ICU (hospital mortality 10.6%). The developed prediction model had an area under the receiver operating characteristic curve 0.886, 95% confidence interval (CI) 0.882-0.890; a calibration slope 1.01, 95% CI (0.99-1.03); a mean calibration -0.004, 95% CI (-0.035 to 0.027). Although the model showed very good internal validity and geographic discrimination transportability, we found substantial heterogeneity of performance measures between ICUs (I-squared: 53.4-84.7%). CONCLUSION: A novel framework evaluating the performance of our prediction model provided key information to judge the validity of our model and its adaptation for future use.

Increased Incidence of Ventilator-Acquired Pneumonia in Coronavirus Disease 2019 Patients: A Multicentric Cohort Study.

OBJECTIVES: Little is known about the epidemiology of ventilator-acquired pneumonia among coronavirus disease 2019 patients such as incidence or etiological agents. Some studies suggest a higher risk of ventilator-associated pneumonia in this specific population. DESIGN: Cohort exposed/nonexposed study among the REA-REZO surveillance network. SETTING: Multicentric; ICUs in France. PATIENTS: The coronavirus disease 2019 patients at admission were matched on the age, sex, center of inclusion, presence of antimicrobial therapy at admission, patient provenance, time from ICU admission to mechanical ventilation, and Simplified Acute Physiology Score II at admission to the patients included between 2016 and 2019 within the same surveillance network (1:1). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The overall incidence of ventilator-associated pneumonia, the cumulative incidence, and hazard rate of the first and the second ventilator-associated pneumonia were estimated. In addition, the ventilator-associated pneumonia microbiological ecology and specific resistant pattern in coronavirus disease 2019 exposed and nonexposed patients were compared. Medication data were not collected. A total of 1,879 patients were included in each group. The overall incidence of ventilator-associated pneumonia was higher among coronavirus disease 2019 exposed patients (25.5; 95% CI [23.7-27.45] vs 15.4; 95% CI [13.7-17.3] ventilator-associated pneumonia per 1,000 ventilation days). The cumulative incidence was higher for the first and the second ventilator-associated pneumonia among the coronavirus disease 2019 exposed patients (respective Gray test p < 0.0001 and 0.0167). The microbiological ecology and resistance were comparable between groups with a predominance of Enterobacterales and nonfermenting Gram-negative bacteria. The documented resistance pattern was similar between groups, except for a lower rate of methicillin-resistant Staphylococcus aureus in the coronavirus disease 2019 exposed patient (6% vs 23%; p = 0.013). CONCLUSIONS: There was a higher incidence of ventilator-associated pneumonia occurring among coronavirus disease 2019 patient compared with the general ICU population, with a similar microbiological ecology and resistance pattern.