scholarly journals Temporal Validation of a Predictive Score for Death in Children with Visceral Leishmaniasis

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Juliana Foinquinos ◽  
Maria do Carmo Duarte ◽  
Jose Natal Figueiroa ◽  
Jailson B. Correia ◽  
Nara Vasconcelos Cavalcanti
Keyword(s):  
Visceral Leishmaniasis ◽  
Predictive Model ◽  
Roc Curve ◽  
Validation Cohort ◽  
Hospital Setting ◽  
Predictive Score ◽  
Convenience Sample ◽  
Derivation Cohort ◽  
Temporal Validation ◽  
Lemeshow Test

Objectives. To perform a temporal validation of a predictive model for death in children with visceral leishmaniasis (VL). Methods. A temporal validation of a children-exclusive predictive model of death due to VL (Sampaio et al. 2010 model), using a retrospective cohort, hereby called validation cohort. The validation cohort convenience sample was made of 156 patients less than 15 years old hospitalized between 2008 and 2018 with VL. Patients included in the Sampaio et al. 2010 study are here denominated derivation cohort, which was composed of 546 patients hospitalized in the same hospital setting in the period from 1996 to 2006. The calibration and discriminative capacity of the model to predict death by VL in the validation cohort were then assessed through the procedure of logistic recalibration that readjusted its coefficients. The calibration of the updated model was tested using Hosmer–Lemeshow test and Spiegelhalter test. A ROC curve was built and the value of the area under this curve represented the model’s discrimination. Results. The validation cohort found a lethality of 6.4%. The Sampaio et al. 2010 model demonstrated inadequate calibration in the validation cohort (Spiegelhalter test: p = 0.007 ). It also presented unsatisfactory discriminative capacity, evaluated by the area under the ROC curve = 0.618. After the coefficient readjustment, the model showed adequate calibration (Spiegelhalter test, p = 0.988 ) and better discrimination, becoming satisfactory (AUROC = 0.762). The score developed by Sampaio et al. 2010 attributed 1 point to the variables dyspnea, associated infections, and neutrophil count <500/mm3; 2 points to jaundice and mucosal bleeding; and 3 points to platelet count <50,000/mm3. In the recalibrated model, each one of the variables had a scoring of 1 point for each. Conclusion. The temporally validated model, after coefficient readjustment, presented adequate calibration and discrimination to predict death in children hospitalized with VL.

scholarly journals Development of an Automated, Real Time Surveillance Tool for Predicting Readmissions at a Community Hospital

2013 ◽  
Vol 04 (02) ◽  
pp. 153-169 ◽  
Author(s):  
R. Gildersleeve ◽  
P. Cooper
Keyword(s):  
Real Time ◽  
Community Hospital ◽  
Validation Cohort ◽  
Data Extraction ◽  
Characteristic Curve ◽  
Hospital Setting ◽  
Data Repository ◽  
Derivation Cohort ◽  
Effective Interventions ◽  
Patients At Risk

SummaryBackground: The Centers for Medicare and Medicaid Services’ Readmissions Reduction Program adjusts payments to hospitals based on 30-day readmission rates for patients with acute myocardial infarction, heart failure, and pneumonia. This holds hospitals accountable for a complex phenomenon about which there is little evidence regarding effective interventions. Further study may benefit from a method for efficiently and inexpensively identifying patients at risk of readmission. Several models have been developed to assess this risk, many of which may not translate to a U.S. community hospital setting.Objective: To develop a real-time, automated tool to stratify risk of 30-day readmission at a semi-rural community hospital.Methods: A derivation cohort was created by extracting demographic and clinical variables from the data repository for adult discharges from calendar year 2010. Multivariate logistic regression identified variables that were significantly associated with 30-day hospital readmission. Those variables were incorporated into a formula to produce a Risk of Readmission Score (RRS). A validation cohort from 2011 assessed the predictive value of the RRS. A SQL stored procedure was created to calculate the RRS for any patient and publish its value, along with an estimate of readmission risk and other factors, to a secure intranet site.Results: Eleven variables were significantly associated with readmission in the multivariate analysis of each cohort. The RRS had an area under the receiver operating characteristic curve (c-statistic) of 0.74 (95% CI 0.73-0.75) in the derivation cohort and 0.70 (95% CI 0.69-0.71) in the validation cohort.Conclusion: Clinical and administrative data available in a typical community hospital database can be used to create a validated, predictive scoring system that automatically assigns a probability of 30-day readmission to hospitalized patients. This does not require manual data extraction or manipulation and uses commonly available systems. Additional study is needed to refine and confirm the findings.Citation: Gildersleeve R, Cooper P. Development of an automated, real time surveillance tool for predicting readmissions at a community hospital. Appl Clin Inf 2013; 4: 153–169http://dx.doi.org/10.4338/ACI-2012-12-RA-0058

scholarly journals Derivation and Validation of Essential Predictors and Risk Index for Early Detection of Diabetic Retinopathy Using Electronic Health Records

2021 ◽  
Vol 10 (7) ◽  
pp. 1473
Author(s):  
Ru Wang ◽  
Zhuqi Miao ◽  
Tieming Liu ◽  
Mei Liu ◽  
Kristine Grdinovac ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection ◽  
Electronic Health Records ◽  
Predictive Model ◽  
Validation Cohort ◽  
Risk Index ◽  
Diabetic Patients ◽  
Health Records ◽  
Derivation Cohort ◽  
Electronic Health

Diabetic retinopathy (DR) is a leading cause for blindness among working-aged adults. The growing prevalence of diabetes urges for cost-effective tools to improve the compliance of eye examinations for early detection of DR. The objective of this research is to identify essential predictors and develop predictive technologies for DR using electronic health records. We conducted a retrospective analysis on a derivation cohort with 3749 DR and 94,127 non-DR diabetic patients. In the analysis, an ensemble predictor selection method was employed to find essential predictors among 26 variables in demographics, duration of diabetes, complications and laboratory results. A predictive model and a risk index were built based on the selected, essential predictors, and then validated using another independent validation cohort with 869 DR and 6448 non-DR diabetic patients. Out of the 26 variables, 10 were identified to be essential for predicting DR. The predictive model achieved a 0.85 AUC on the derivation cohort and a 0.77 AUC on the validation cohort. For the risk index, the AUCs were 0.81 and 0.73 on the derivation and validation cohorts, respectively. The predictive technologies can provide an early warning sign that motivates patients to comply with eye examinations for early screening and potential treatments.

scholarly journals Application Value of Rapid Predictive Model for Readmission Risk in Patients after CABG

2020 ◽  
Vol 23 (5) ◽  
pp. E668-E672
Author(s):  
Tiao Lv ◽  
Yinghong Zhang ◽  
Wen Zhang ◽  
Liu Hu ◽  
Guozhen Liu ◽  
...  
Keyword(s):  
Predictive Model ◽  
Roc Curve ◽  
Test Results ◽  
Good Discrimination ◽  
C Statistic ◽  
Lemeshow Test ◽  
Area Under Roc Curve ◽  
Cabg Patients ◽  
Readmission Risk ◽  
Heart Hospital

Objective: To explore the value of a rapid risk predictive model for the readmission of patients after CABG in China. Methods: The rapid predictive model of readmission risk was translated into Chinese, and then validated with data from 758 patients who underwent CABG in Wuhan Asian Heart Hospital from January 2018 to June 2019. The discrimination was tested by area under the ROC curve (AUC), and the calibration was tested by Hosmer-Lemeshow test. Results: The rapid risk predictive model for readmission showed good discrimination and calibration in Chinese CABG patients (The area under ROC curve c-statistic: 0.704, 95% CI: 0.614-0.794; Hosmer-Lemeshow test: P = .955). Conclusion: The rapid readmission risk predictive model can be used in Chinese CABG patients soon after admission.

Reproducibility of the Penn predictive score of tumor lysis syndrome (PPS-TLS) in acute myelogenous leukemia (AML)

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 6577-6577 ◽  
Author(s):  
A. R. Mato ◽  
A. N. Miltiades ◽  
M. Guo ◽  
D. F. Heitjan ◽  
M. P. Carroll ◽  
...  
Keyword(s):  
Predictive Model ◽  
Induction Chemotherapy ◽  
Roc Curve ◽  
Univariate Analysis ◽  
Tumor Lysis Syndrome ◽  
Elevated Serum ◽  
Myelogenous Leukemia ◽  
Predictive Score ◽  
Baseline Serum ◽  
Tumor Lysis

6577 Background: In a previous retrospective study of 194 patients undergoing AML induction chemotherapy, we described a TLS predictive model entitled the Penn Predictive Score for Tumor Lysis Syndrome (PPS-TLS). TLS incidence was 9.8%. The PPS-TLS is defined as the sum of the scores for pre-induction lactate dehydrogenase (LDH), uric acid (UA), and gender (see table ). The area under the receiver operator characteristic (ROC) curve for this cohort was 89% (SD 4%). Methods: To validate the PPS-TLS, we retrospectively analyzed a second dataset of 166 AML patients undergoing induction chemotherapy from 2003–2006 at our institution. All patients received TLS prophylaxis. TLS was defined as (i) doubling of baseline serum creatinine (Cr) in association with elevated serum phosphate, UA, or potassium or (ii) elevations in 2 of the above electrolytes within 7 days of initiation of therapy. Potential TLS predictive factors were analyzed for statistical significance. Results: In this new dataset, TLS incidence is 9.6%. Significant in univariate analysis are male sex (OR=6.0, CI 1.31–27.15), Cr (OR=13.0, CI 2.88–58.23), UA (OR=48.6, CI 5.78–408.95), and LDH (OR=1.2, CI 1.03–1.48). In multivariate analysis, LDH (OR=1.3, CI 1.04–1.70) and Cr (OR=6.8, CI 1.48–30.89) remain significant. PPS-TLS scores were calculated and tested for their ability to predict TLS in this dataset. The area under the ROC curve for the PPS-TLS in this dataset was 75% (CI 61%-89%), indicating that the probability that a patient with TLS would have a higher PPS-TLS score than one without TLS is 75%. The current result is not statistically significantly different from the area under the ROC curve in the initial dataset (89%). Conclusions: The PPS-TLS is the first TLS predictive model in AML. The reproducibility of this model is supported by this study. A prospective multisite study is being designed to further validate this model. This analysis may lay the groundwork for the first evidence-based guidelines for TLS monitoring and management in AML. [Table: see text] No significant financial relationships to disclose.

scholarly journals Predictive Model for the Risk of Severe Acute Malnutrition in Children

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Olivier Mukuku ◽  
Augustin Mulangu Mutombo ◽  
Lewis Kipili Kamona ◽  
Toni Kasole Lubala ◽  
Paul Makan Mawaw ◽  
...  
Keyword(s):  
Developing Countries ◽  
Predictive Model ◽  
Roc Curve ◽  
Severe Acute Malnutrition ◽  
Well Being ◽  
Physical Growth ◽  
Acute Malnutrition ◽  
Predictive Score ◽  
History Of ◽  
Children Under 5

Background. The nutritional status is the best indicator of the well-being of the child. Inadequate feeding practices are the main factors that affect physical growth and mental development. The aim of this study was to develop a predictive score of severe acute malnutrition (SAM) in children under 5 years of age.Methods. It was a case-control study. The case group (n = 263) consisted of children aged 6 to 59 months admitted to hospital for SAM that was defined by az-score weight/height < −3 SD or presence of edema of malnutrition. We performed a univariate and multivariate analysis. Discrimination score was assessed using the ROC curve and the calibration of the score by Hosmer–Lemeshow test.Results. Low birth weight, history of recurrent or chronic diarrhea, daily meal’s number less than 3, age of breastfeeding’s cessation less than 6 months, age of introduction of complementary diets less than 6 months, maternal age below 25 years, parity less than 5, family history of malnutrition, and number of children under 5 over 2 were predictive factors of SAM. Presence of these nine criteria affects a certain number of points; a score <6 points defines children at low risk of SAM, a score between 6 and 8 points defines a moderate risk of SAM, and a score >8 points presents a high risk of SAM. The area under ROC curve of this score was 0.9685, its sensitivity was 93.5%, and its specificity was 93.1%.Conclusion. We propose a simple and efficient prediction model for the risk of occurrence of SAM in children under 5 years of age in developing countries. This predictive model of SAM would be a useful and simple clinical tool to identify people at risk, limit high rates of malnutrition, and reduce disease and child mortality registered in developing countries.

scholarly journals Development and validation of a predictive score for ICU delirium in critically ill patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Huijuan Zhang ◽  
Jing Yuan ◽  
Qun Chen ◽  
Yingya Cao ◽  
Zhen Wang ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Multivariate Logistic Regression Analysis ◽  
Predictive Score ◽  
Clinical Indicators ◽  
Derivation Cohort ◽  
Significant Difference ◽  
History Of ◽  
Icu Delirium ◽  
Risk Predictors ◽  
Development And Validation

Abstract Background The incidence of delirium in intensive care unit (ICU) patients is high and associated with a poor prognosis. We validated the risk factors of delirium to identify relevant early and predictive clinical indicators and developed an optimized model. Methods In the derivation cohort, 223 patients were assigned to two groups (with or without delirium) based on the CAM-ICU results. Multivariate logistic regression analysis was conducted to identify independent risk predictors, and the accuracy of the predictors was then validated in a prospective cohort of 81 patients. Results A total of 304 patients were included: 223 in the derivation group and 81 in the validation group, 64(21.1%)developed delirium. The model consisted of six predictors assessed at ICU admission: history of hypertension (RR = 4.367; P = 0.020), hypoxaemia (RR = 3.382; P = 0.018), use of benzodiazepines (RR = 5.503; P = 0.013), deep sedation (RR = 3.339; P = 0.048), sepsis (RR = 3.480; P = 0.018) and mechanical ventilation (RR = 3.547; P = 0.037). The mathematical model predicted ICU delirium with an accuracy of 0.862 (P < 0.001) in the derivation cohort and 0.739 (P < 0.001) in the validation cohort. No significant difference was found between the predicted and observed cases of ICU delirium in the validation cohort (P > 0.05). Conclusions Patients’ risk of delirium can be predicted at admission using the early prediction score, allowing the implementation of early preventive interventions aimed to reduce the incidence and severity of ICU delirium.

scholarly journals Development and validation of a predictive model for end-stage renal disease risk in patients with diabetic nephropathy confirmed by renal biopsy

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8499 ◽  
Author(s):  
Lulu Sun ◽  
Jin Shang ◽  
Jing Xiao ◽  
Zhanzheng Zhao
Keyword(s):  
End Stage Renal Disease ◽  
Validation Cohort ◽  
Pathological Grade ◽  
Clinical Model ◽  
Derivation Cohort ◽  
Pathological Model ◽  
Lemeshow Test ◽  
Stage Renal Disease ◽  
End Stage

This study was performed to develop and validate a predictive model for the risk of end-stage renal disease (ESRD) inpatients with diabetic nephropathy (DN) confirmed by renal biopsy. We conducted a retrospective study with 968 patients with T2DM who underwentrenal biopsy for the pathological confirmation of DNat the First Affiliated Hospital of Zhengzhou University from February 2012 to January 2015; the patients were followed until December 2018. The outcome was defined as a fatal or nonfatal ESRD event (peritoneal dialysis or hemodialysis for ESRD, renal transplantation, or death due to chronic renal failure or ESRD). The dataset was randomly split into development (75%) and validation (25%) cohorts. We used stepwise multivariablelogistic regression to identify baseline predictors for model development. The model’s performance in the two cohorts, including discrimination and calibration, was evaluated by the C-statistic and the P value of the Hosmer-Lemeshow test. During the 3-year follow-up period, there were 225 outcome events (47.1%) during follow-up. Outcomes occurred in 187 patients (52.2%) in the derivation cohort and 38 patients (31.7%) in the validation cohort. The variables selected in the final multivariable logistic regression after backward selection were pathological grade, Log Urinary Albumin-to-creatinine ratio (Log ACR), cystatin C, estimated glomerular filtration rate (eGFR) and B-type natriuretic peptide (BNP). 4 prediction models were created in a derivation cohort of 478 patients: a clinical model that included cystatin C, eGFR, BNP, Log ACR; a clinical-pathological model and a clinical-medication model, respectively, also contained pathological grade and renin-angiotensin system blocker (RASB) use; and a full model that also contained the pathological grade, RASB use and age. Compared with the clinical model, the clinical-pathological model and the full model had better C statistics (0.865 and 0.866, respectively, vs. 0.864) in the derivation cohort and better C statistics (0.876 and 0.875, respectively, vs. 0.870) in the validation cohort. Among the four models, the clinical-pathological model had the lowest AIC of 332.53 and the best P value of 0.909 of the Hosmer-Lemeshow test. We constructed a nomogram which was a simple calculator to predict the risk ratio of progression to ESRD for patients with DN within 3 years. The clinical-pathological model using routinely available clinical measurements was shown to be accurate and validated method for predicting disease progression in patients with DN. The risk model can be used in clinical practice to improve the quality of risk management and early intervention.

scholarly journals Predischarge Prediction of Readmission After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy: Derivation and Validation of a Risk Prediction Score

2021 ◽  
Author(s):  
Caroline J. Rieser ◽  
Lauren B. Hall ◽  
Eliza Kang ◽  
Amer H. Zureikat ◽  
Matthew P. Holtzman ◽  
...  
Keyword(s):  
High Risk ◽  
Intraperitoneal Chemotherapy ◽  
Cytoreductive Surgery ◽  
Goodness Of Fit ◽  
Hyperthermic Intraperitoneal Chemotherapy ◽  
Validation Cohort ◽  
Predictive Score ◽  
Unplanned Readmission ◽  
Discharge Data ◽  
Derivation Cohort

Abstract Background Ninety-day hospital readmission rates following cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS/HIPEC) range from 20 to 40%. Objective The aim of this study was to develop and validate a simple score to predict readmissions following CRS/HIPEC. Study Design Using a prospectively maintained database, we retrospectively reviewed clinicopathologic, perioperative, and day-of-discharge data for patients undergoing CRS/HIPEC for peritoneal surface malignancies between 2010 and 2018. In-hospital mortalities and discharges to hospice were excluded. Multivariate logistic regression was utilized to identify predictors of unplanned readmission, with three-quarters of the sample randomly selected as the derivation cohort and one-quarter as the validation cohort. Using regression coefficient-based scoring methods, we developed a weighted 7-factor, 10-point predictive score for risk of readmission. Results Overall, 1068 eligible discharges were analyzed; 379 patients were readmitted within 90 days (35.5%). Seven factors were associated with readmission: stoma creation, Peritoneal Cancer Index score ≥ 15, hyponatremia, in-hospital major complication, preoperative chemotherapy, anemia, and discharge to nursing home. In the validation cohort, 25 patients (9.2%) were categorized as high risk for readmission, with a predicted rate of readmission of 69.3% and an observed rate of 76.0%. The score had fair discrimination (area under the curve 0.70) and good calibration (Hosmer–Lemeshow goodness-of-fit p-value of 0.77). Conclusion Our proposed risk score, easily obtainable on day of discharge, distinguishes patients at high risk for readmission over 90 days following CRS/HIPEC. This score has the potential to target high-risk individuals for intensive follow-up and other interventions.

Abstract TMP85: Predictive Score of Hemorrhagic Transformation in Patients Not Submitted to Reperfusion Therapies - PROpHET

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Joao B Andrade ◽  
Gisele S Silva ◽  
Jay P Mohr ◽  
Joao J Carvalho ◽  
Luisa Franciscatto ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Reperfusion Therapy ◽  
Predictive Score ◽  
Brier Score ◽  
Multivariable Logistic Regression Model ◽  
Derivation Cohort ◽  
Digital Version

Objective: To create an accurate and user-friendly pr edictive sc o re for he morrhagic t ransformation in patients not submitted to reperfusion therapies (PROpHET). Methods: We created a multivariable logistic regression model to assess the prediction of Hemorrhage Transformation (HT) for acute ischemic strokes not treated with reperfusion therapy. One point was assigned for each of gender, cardio-aortic embolism, hyperdense middle cerebral artery sign, leukoaraiosis, hyperglycemia, 2 points for ASPECTS ≤7, and -3 points for lacunar syndrome. Acute ischemic stroke patients admitted to the Fortaleza Comprehensive Stroke Center in Brazil from 2015 to 2017 were randomly selected to the derivation cohort. The validation cohort included similar, but not randomized, cases from 5 Brazilian and one American Comprehensive Stroke Centers. Symptomatic cases were defined as NIHSS ≥4 at 24 hours after the event. Results from the derivation and validation cohorts were assessed with the area under the receiver operating characteristic curve (AUC-ROC). Results: From 2,432 of acute ischemic stroke screened in Fortaleza, 448 were prospectively selected for the derivation cohort and a 7-day follow-up. From 1,847 not selected, 577 underwent reperfusion therapy, 734 were excluded due to inadequate imaging or refusal of consent, and 538 whose data were obtained retrospectively and were selected only for the validation cohort. A score ≥3 had 78% sensitivity and 75% specificity, AUC-ROC 0.82 for all cases of HT, Hosmer-Lemeshow 0.85, Brier Score 0.1, and AUC-ROC 0.83 for those with symptomatic HT. An AUC-ROC of 0.84 was found for the validation cohort of 1,910 from all 6 centers, and a score ≥3 was found in 65% of patients with HT against 11.3% of those without HT. In comparison with 8 published predictive scores of HT, PROpHET was the most accurate (p < 0.01). Conclusions: PROpHET offers a tool simple, quick and easy-to-perform to estimate risk stratification of HT in patients not submitted to RT. A digital version of PROpHET is available in www.score-prophet.com Classification of evidence: This study provides Class I evidence from prospective data acquisition.

scholarly journals Prediction of extubation outcome in mechanically ventilated patients: Development and validation of the Extubation Predictive Score (ExPreS)

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248868
Author(s):  
Antuani Rafael Baptistella ◽  
Laura Maito Mantelli ◽  
Leandra Matte ◽  
Maria Eduarda da Rosa Ulanoski Carvalho ◽  
João Antonio Fortunatti ◽  
...  
Keyword(s):  
Roc Analysis ◽  
Validation Cohort ◽  
Extubation Failure ◽  
Predictive Score ◽  
Mechanically Ventilated ◽  
Derivation Cohort ◽  
Probability Of Success ◽  
Extubation Outcome ◽  
Auc Value ◽  
Ventilated Patients

Despite the best efforts of intensive care units (ICUs) professionals, the extubation failure rates in mechanically ventilated patients remain in the range of 5%–30%. Extubation failure is associated with increased risk of death and longer ICU stay. This study aimed to identify respiratory and non-respiratory parameters predictive of extubation outcome, and to use these predictors to develop and validate an “Extubation Predictive Score (ExPreS)” that could be used to predict likelihood of extubation success in patients receiving invasive mechanical ventilation (IMV). Derivation cohort was composed by patients aged ≥18 years admitted to the ICU and receiving IMV through an endotracheal tube for >24 hours. The weaning process followed the established ICU protocol. Clinical signs and ventilator parameters of patients were recorded during IMV, in the end phase of weaning in pressure support ventilation (PSV) mode, with inspiratory pressure of 7 cm H2O over the PEEP (positive end expiratory pressure). Patients who tolerated this ventilation were submitted to spontaneous breathing trial (SBT) with T-tube for 30 minutes. Those who passed the SBT and a subsequent cuff-leak test were extubated. The primary outcome of this study was extubation success at 48 hours. Parameters that showed statistically significant association with extubation outcome were further investigated using the receiver operating characteristics (ROC) analysis to assess their predictive value. The area under the curve (AUC) values were used to select parameters for inclusion in the ExPreS. Univariable logistic regression analysis and ROC analysis were performed to evaluate the performance of ExPreS. Patients’ inclusion and statistical analyses for the prospective validation cohort followed the same criteria used for the derivation cohort and the decision to extubate was based on the ExPreS result. In the derivation cohort, a total of 110 patients were extubated: extubation succeeded in 101 (91.8%) patients and failed in 9 (8.2%) patients. Rapid shallow-breathing index (RSBI) in SBT, dynamic lung compliance, duration of IMV, muscle strength, estimated GCS, hematocrit, and serum creatinine were significantly associated with extubation outcome. These parameters, along with another parameter—presence of neurologic comorbidity—were used to create the ExPreS. The AUC value for the ExPreS was 0.875, which was higher than the AUCs of the individual parameters. The total ExPreS can range from 0 to 100. ExPreS ≥59 points indicated high probability of success (OR = 23.07), while ExPreS ≤44 points indicated low probability of success (OR = 0.82). In the prospective validation cohort, 83 patients were extubated: extubation succeeded in 81 (97.6%) patients and failed in 2 (2.4%) patients. The AUC value for the ExPreS in this cohort was 0.971. The multiparameter score that we propose, ExPreS, shows good accuracy to predict extubation outcome in patients receiving IMV in the ICU. In the prospective validation, the use of ExPreS decreased the extubation failure rate from 8.2% to 2.4%, even in a cohort of more severe patients.

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