scholarly journals Development and validation of a score to predict mortality in ICU patients with sepsis: a multicenter retrospective study

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jie Weng ◽  
Ruonan Hou ◽  
Xiaoming Zhou ◽  
Zhe Xu ◽  
Zhiliang Zhou ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Metastatic Cancer ◽  
Demographic Data ◽  
Mortality Prediction ◽  
Prediction Score ◽  
Icu Mortality ◽  
Goodness Of Fit Test ◽  
Icu Patients ◽  
Development And Validation ◽  
Apache Iv

Abstract Background Early and accurate identification of septic patients at high risk for ICU mortality can help clinicians make optimal clinical decisions and improve the patients’ outcomes. This study aimed to develop and validate (internally and externally) a mortality prediction score for sepsis following admission in the ICU. Methods We extracted data retrospectively regarding adult septic patients from one teaching hospital in Wenzhou, China and a large multi-center critical care database from the USA. Demographic data, vital signs, laboratory values, comorbidities, and clinical outcomes were collected. The primary outcome was ICU mortality. Through multivariable logistic regression, a mortality prediction score for sepsis was developed and validated. Results Four thousand two hundred and thirty six patients in the development cohort and 8359 patients in three validation cohorts. The Prediction of Sepsis Mortality in ICU (POSMI) score included age ≥ 50 years, temperature < 37 °C, Respiratory rate > 35 breaths/min, MAP ≤ 50 mmHg, SpO2 < 90%, albumin ≤ 2 g/dL, bilirubin ≥ 0.8 mg/dL, lactate ≥ 4.2 mmol/L, BUN ≥ 21 mg/dL, mechanical ventilation, hepatic failure and metastatic cancer. In addition, the area under the receiver operating characteristic curve (AUC) for the development cohort was 0.831 (95% CI, 0.813–0.850) while the AUCs ranged from 0.798 to 0.829 in the three validation cohorts. Moreover, the POSMI score had a higher AUC than both the SOFA and APACHE IV scores. Notably, the Hosmer–Lemeshow (H–L) goodness-of-fit test results and calibration curves suggested good calibration in the development and validation cohorts. Additionally, the POSMI score still exhibited excellent discrimination and calibration following sensitivity analysis. With regard to clinical usefulness, the decision curve analysis (DCA) of POSMI showed a higher net benefit than SOFA and APACHE IV in the development cohort. Conclusion POSMI was validated to be an effective tool for predicting mortality in ICU patients with sepsis.

scholarly journals COVID-19 Impact on Acute Ischemic Stroke Treatment at 9 Comprehensive Stroke Centers across Los Angeles

2021 ◽  
pp. 1-8
Author(s):  
M. Maximillian Padrick ◽  
Navdeep Sangha ◽  
Laurie Paletz ◽  
James Mirocha ◽  
Sonia Figueroa ◽  
...  
Keyword(s):  
Los Angeles ◽  
Goodness Of Fit ◽  
Demographic Data ◽  
Lower Percentage ◽  
Stroke Severity ◽  
Goodness Of Fit Test ◽  
Skilled Nursing ◽  
Stroke Incidence ◽  
Stroke Treatment ◽  
The Impact

<b><i>Objective:</i></b> To describe the impact of COVID-19 on acute cerebrovascular disease care across 9 comprehensive stroke centers throughout Los Angeles County (LAC). <b><i>Methods:</i></b> Volume of emergency stroke code activations, patient characteristics, stroke severity, reperfusion rates, treatment times, and outcomes from February 1 to April 30, 2020, were compared against the same time period in 2019. Demographic data were provided by each participating institution. <b><i>Results:</i></b> There was a 17.3% decrease in stroke code activations across LAC in 2020 compared to 2019 (1,786 vs. 2,159, respectively, χ<sup>2</sup> goodness of fit test <i>p</i> &#x3c; 0.0001) across 9 participating comprehensive stroke centers. Patients who did not receive any reperfusion therapy decreased by 16.6% in 2020 (1,527) compared to 2019 (1,832). Patients who received only intravenous thrombolytic (IVT) therapy decreased by 31.8% (107 vs. 157). Patients who received only mechanical thrombectomy (MT) increased by 3% (102 vs. 99). Patients who received both IVT and MT decreased by 31.8% (45 vs. 66). Recanalization treatment times in 2020 were comparable to 2019. CSCs serving a higher proportion of Latinx populations in the eastern parts of LAC experienced a higher incidence of MT in 2020 compared to 2019. Mild increase in stroke severity was seen in 2020 compared to 2019 (8.95 vs. 8.23, <i>p</i> = 0.046). A higher percentage of patients were discharged home in 2020 compared to 2019 (59.5 vs. 56.1%, <i>p</i> = 0.034), a lower percentage of patients were discharged to skilled nursing facility (16.1 vs. 20.7%, <i>p</i> = 0.0004), and a higher percentage of patients expired (8.6 vs. 6.3%, <i>p</i> = 0.008). <b><i>Conclusion:</i></b> LAC saw a decrease in overall stroke code activations in 2020 compared to 2019. Reperfusion treatment times remained comparable to prepandemic metrics. There has been an increase in severe stroke incidence and higher volume of thrombectomy treatments in Latinx communities within LAC during the pandemic of 2020. More patients were discharged home, less patients discharged to skilled nursing facilities, and more patients expired in 2020, compared to the same time frame in 2019.

scholarly journals Students’ perception regarding skill lab training in medical education

2019 ◽  
Vol 26 (06) ◽  
Author(s):  
Shezadi Sabah Imran ◽  
Musarat Ramzan ◽  
Fatima Tuz Zahra ◽  
Farhana Kausar ◽  
Benish Khan ◽  
...  
Keyword(s):  
Medical Education ◽  
Medical Students ◽  
Goodness Of Fit ◽  
Medical Curriculum ◽  
Clinical Skills ◽  
Demographic Data ◽  
Clinical Skill ◽  
P Value ◽  
Goodness Of Fit Test ◽  
Cross Sectional

Introduction: Clinical skills refer to the skills required for a clinician to manage a complete patient encounter. Clinical skill laboratories provide the facility to medical students and medical staff to learn the clinical skills before applying them on patients. Objectives: To evaluate perception of medical students regarding skill lab training. Study Design: Cross -sectional study. Study Setting: Wah Medical College. Period: January 2017 to June 2017. Study Subject: Students of Final Year M.B.B.S. Sample Size: 114 students. Sampling Technique: Convenient sampling. Data Collection Procedure: With informed consent of participants, questionnaires were filled by students themselves. Questionnaire was comprised of two parts; first part comprised of demographic data, second part comprised of 18 questions to determine perception of medical students about skill lab training. The responses of 18 questions were measured on four-point Likert scale from strongly disagree to agree. Data Analysis: Data was analyzed by using SPSS version 19, frequencies and percentages were calculated. The Chi square- goodness of fit test for one sample was applied on various levels of agreement. The p value of less than 0.05 was considered as significant. Results: The mean age of 114 students was 23.4 years with minimum age of 21 years and maximum of 26 years. Male students were 45(39%) and 69(61%) were female students. Out of 114 medical students108 (94.8%) students preferred to practice in skill lab before performing it on patient and they also had an opinion that the mentor must be friendly and helpful during teaching. Among them 107 (93.9%) students desired that procedures in the skill lab should be performed by the mentors first in front of students and 103 (90.4%) students thought that training of practical skills improve their learning. Out of them 94(82.5%) students believed that skill lab training increased their motivation to become a doctor, 102 (89.5%) students thought that skill lab practice provides a feeling of security for learning process and 100 (87.7%) students had an opinion that it should be a compulsory part of medical curriculum and even it should be started from the first year of the medical education. P value of level of agreement of all the variables regarding perception about skill lab training was found to be < 0.001 which was statistically significant. Conclusion: The students believed that skill lab training is very useful for them and they preferred to practice on manikin before dealing with the patients.

scholarly journals Dynamic APACHE II Score to Predict Outcome Among Intensive Care Unit Patients

2021 ◽  
Author(s):  
Yao Tian ◽  
Yang YAO ◽  
Jing Zhou ◽  
Xin Diao ◽  
Hui Chen ◽  
...  
Keyword(s):  
Intensive Care ◽  
Hospital Mortality ◽  
Goodness Of Fit ◽  
Apache Ii ◽  
Secondary Outcome ◽  
Apache Ii Score ◽  
Optimal Timing ◽  
Goodness Of Fit Test ◽  
Icu Patients ◽  
Mimic Iii

Abstract Purpose: The Acute Physiology and Chronic Health Evaluation II (APACHE II) score is used to determine disease severity and predict outcomes in critically ill patients. However, there is no dynamic APACHE II score for predicting outcomes among ICU patients.The aim of this study is to explore the optimal timing to predict the outcomes of ICU patients by dynamically evaluating APACHE II score.Methods: Study data of demographics and comorbidities from the first 24 h after ICU admission were retrospectively extracted from MIMIC-III, a multiparameter intensive care database. The primary outcome was hospital mortality. 90-day mortality was a secondary outcome. APACHE II scores on days 1, 2, 3, 5, 7, 14 and 28 were compared using area under the receiver operating characteristic (AUROC) analysis. Hospital survival was visualised using Kaplan-Meier Curves.Results:A total of 6374 eligible subjects were extracted from the MIMIC-III. Mean APACHE II score on day 1 were 18.4±6.3, hospital and 90-day mortality was 19.1% and 25.8%, respectively.The optimal timing where predicted hospital mortality was on day 3 with an area under the cure of 0.666 (0.607-0.726)(P＜0.0001). The best tradeoff for preciction was found at 17 score, more than 17 score predicted mortality of non-survivors with a sensitivity of 92.8% and PPV of 23.1%. Hosmer-lemeshow goodness of fit test showed that APACHE II 3 has a good predictive calibration ability (X2 =6.198, P=0.625) and consistency of predicted death and actual death was 79.4%. The calibration of APACHE II 1 was poor (X2=294.898, P<0.001).Conclusions: APACHE II on 3 dayis the optimal prognostic marker and 17 score provided the best dignostic accuracy to predict outcomes for ICU patients. These finding will help medical make clinical judgment.

scholarly journals Development and Validation of a 90-day Mortality Prediction Nomogram for AMI Patients: A Retrospective Cohort Study

2021 ◽  
Author(s):  
Rui Yang ◽  
Wen Ma ◽  
Tao Huang ◽  
Lu-Ming Zhang ◽  
Di-Di Han ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Random Forest ◽  
Goodness Of Fit ◽  
Risk Model ◽  
Mortality Prediction ◽  
Multiple Logistic Regression ◽  
Goodness Of Fit Test ◽  
Standard Curve ◽  
Mimic Iii

Abstract Background: The purpose of this study was to identify the factors influencing the 90-day mortality of acute myocardial infarction(AMI) patients, and to establish a prognostic model for these patients based on the MIMIC-III database.Methods: Retrospective study methods were used to collect AMI patient data that met the inclusion criteria from the MIMIC-III database. Variable importance selection was determined using the random forest algorithm. Multiple logistic regression was used to determine AMI-related risk factors, with the results represented as a nomogram.Results: The baseline scores for the training and validation groups were very flat, and indicators for developing risk-model nomograms were obtained after random forest and multiple logistic regression. The AUC of the risk model was the highest (0.826 and 0.818 in the training and validation groups, respectively) . The Hosmer-Lemeshow goodness-of-fit test and standard curve both produced very consistent results. Both the NRI and IDI values indicated that the risk model had significant predictive power, and DCA results indicated that the risk model had good net benefits for clinical application.Conclusions: The results of this study indicated that age, troponinT, VT, VFI, MI_his, APS-III, bypass, and PCI were risk factors for 90-day mortality in AMI patients. Interactive nomograms could provide intuitive and concise personalized 90-day mortality predictions for AMI patients.

scholarly journals The development of using nomogram to predict pregnancy outcomes of emergency oocyte freeze-thaw cycles

2020 ◽  
Author(s):  
Yang Wang ◽  
Ziru Niu ◽  
Liyuan Tao ◽  
Xiaoying Zheng ◽  
Yifeng Yuan ◽  
...  
Keyword(s):  
Pregnancy Outcomes ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Model Performance ◽  
Goodness Of Fit Test ◽  
Discriminatory Ability ◽  
Female Age ◽  
Lemeshow Test ◽  
Development And Validation ◽  
Good Calibration

Abstract Background: To study which characteristics of a pre-oocyte-retrieval patient can affect the pregnancy outcomes of emergency oocyte freeze-thaw cycles. Methods: Nomogram model performance was assessed by examining the discrimination and calibration in the development and validation cohorts. Discriminatory ability was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration plots. Data was collected from the Reproductive Center, Peking University Third Hospital of China. Nomogram model performance was assessed by examining the discrimination and calibration in the development and validation cohorts. Discriminatory ability was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration plots.Results: The predictors in the model of ‘no embryo to transfer’ are female age (OR= 1.099, 95% CI=1.003-1.205, P=0.044), duration of infertility(OR= 1.140, 95% CI=1.018-1.276, P=0.024), basal FSH level (OR= 1.205, 95% CI=1.051-1.382, P=0.0084), basal E2 level (OR=1.006, 95% CI=1.001-1.010, P=0.012) and sperm from MESA (OR=7.741, 95% CI=2.905-20.632, P<0.001). Upon assessing predictive ability, the AUC for this model was 0.799 (95% CI: 0.722–0.875, p＜0.001). The Hosmer-Lemeshow test (p=0.721) and calibration curve showed good calibration. The predictors in the cumulative live birth were the number of follicles on the day of hCG administration (OR= 1.088, 95% CI=1.030-1.149, P=0.002) and endometriosis (OR= 0.172, 95% CI=0.035-0.853, P=0.031). The AUC for this model was 0.724 (95% CI: 0.647–0.801, p＜0.001). The Hosmer-Lemeshow test (p=0.562) and calibration curve showed good calibration for the prediction of cumulative live birth. Conclusion: The predictors in the final multivariate logistic regression models found to be significantly associated with poor pregnancy outcomes were increasing female age, duration of infertility, basal FSH and E2 level, the number of follicles with a diameter greater than 10 mm on the day of hCG administration, endometriosis and sperm from microdissection testicular sperm extraction (MESA).

scholarly journals Feature Engineering for ICU Mortality Prediction Based on Hourly to Bi-Hourly Measurements

2019 ◽  
Vol 9 (17) ◽  
pp. 3525 ◽  
Author(s):  
Ahmed Y. A. Amer ◽  
Julie Vranken ◽  
Femke Wouters ◽  
Dieter Mesotten ◽  
Pieter Vandervoort ◽  
...  
Keyword(s):  
Vital Signs ◽  
Feature Space ◽  
Mortality Prediction ◽  
Fine Tuning ◽  
Classification Error ◽  
Support Vector ◽  
Feature Engineering ◽  
Icu Mortality ◽  
Icu Patients ◽  
Engineering Approach

Mortality prediction for intensive care unit (ICU) patients is a challenging problem that requires extracting discriminative and informative features. This study presents a proof of concept for exploring features that can provide clinical insight. Through a feature engineering approach, it is attempted to improve ICU mortality prediction in field conditions with low frequently measured data (i.e., hourly to bi-hourly). Features are explored by investigating the vital signs measurements of ICU patients, labelled with mortality or survival at discharge. The vital signs of interest in this study are heart and respiration rate, oxygen saturation and blood pressure. The latter comprises systolic, diastolic and mean arterial pressure. In the feature exploration process, it is aimed to extract simple and interpretable features that can provide clinical insight. For this purpose, a classifier is required that maximises the margin between the two classes (i.e., survival and mortality) with minimum tolerance to misclassification errors. Moreover, it preferably has to provide a linear decision surface in the original feature space without mapping to an unlimited dimensionality feature space. Therefore, a linear hard margin support vector machine (SVM) classifier is suggested. The extracted features are grouped in three categories: statistical, dynamic and physiological. Each category plays an important role in enhancing classification error performance. After extracting several features within the three categories, a manual feature fine-tuning is applied to consider only the most efficient features. The final classification, considering mortality as the positive class, resulted in an accuracy of 91.56 % , sensitivity of 90.59 % , precision of 86.52 % and F 1 -score of 88.50 % . The obtained results show that the proposed feature engineering approach and the extracted features are valid to be considered and further enhanced for the mortality prediction purpose. Moreover, the proposed feature engineering approach moved the modelling methodology from black-box modelling to grey-box modelling in combination with the powerful classifier of SVMs.

scholarly journals Utilization of Systemic Inflammatory Response Syndrome Criteria in predicting mortality among geriatric patients with influenza in the emergency department

2019 ◽  
Author(s):  
Henry Chih-Hung Tai ◽  
Chien-Chun Yeh ◽  
Yen-An Chen ◽  
Chien-Chin Hsu ◽  
Jiann-Hwa Chen ◽  
...  
Keyword(s):  
Emergency Department ◽  
Systemic Inflammatory Response Syndrome ◽  
Inflammatory Response ◽  
Goodness Of Fit ◽  
Geriatric Patients ◽  
Statistical Significance ◽  
Mortality Prediction ◽  
Systemic Inflammatory Response ◽  
Goodness Of Fit Test ◽  
Sirs Criteria

Abstract Background Systemic Inflammatory Response Syndrome (SIRS) criteria are often used to evaluate the risk of sepsis and to identify in-hospital mortality among patients with suspected infection. However, utilization of the SIRS criteria in mortality prediction among geriatric patients with influenza in the emergency department (ED) remains unclear. Therefore, we conducted a research to delineate this issue. Methods This is a retrospective case–control study including geriatric patients (age ≥ 65 years) with influenza, who presented to the ED of a medical center between January 1, 2010 and December 31, 2015. Vital signs, past history, subtype of influenza, demographic data, and outcomes were collected from all patients and analyzed. We calculated the accuracy for predicting 30-days mortality using the SIRS criteria. We also performed covariate adjustment of the area under the receiver operating characteristic curve (AUROC) via regression modeling. Results We recruited a total of 409 geriatric patients in the ED, with mean age 79.5 years and an equal sex ratio. The mean SIRS criteria score was 1.9±1.1. The result of a Hosmer–Lemeshow goodness-of-fit test was 0.34 for SIRS criteria. SIRS criteria score ≥ 3 showed better mortality prediction, with odds ratio (OR) 3.37 (95% confidence interval (CI), 1.05–10.73); SIRS score ≥ 2 showed no statistical significance, with p = 0.85 (OR, 1.15; 95% CI, 0.28–4.69). SIRS score ≥ 3 had acceptable 30-days mortality discrimination, with AUROC 0.77 (95% CI, 0.68–0.87) after adjustment. SIRS score ≥ 3 also had a notable negative predictive value of 0.97 (95% CI, 0.94–0.99). Conclusion The presence of a higher number of SIRS criteria (≥ 3) showed greater accuracy for predicting mortality among geriatric patients with influenza.

Validation of the CASUS score in Tunisian patients: a quality management tool in intensive care units

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
T Besbes ◽  
S Mleyhi ◽  
J Sahli ◽  
M Messai ◽  
J Ziadi ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
Roc Curve ◽  
Goodness Of Fit ◽  
Cardiovascular Surgery ◽  
Management Tool ◽  
Medical Decision ◽  
Tunisian Population ◽  
Correct Classification ◽  
Goodness Of Fit Test ◽  
A Value

Abstract Background Early prediction of patients at highest risk of a poor outcome after cardiovascular surgery, including death can aid medical decision making, and adapt health care management in order to improve prognosis. In this context, we conducted this study to validate the CASUS severity score after cardiac surgery in the Tunisian population. Methods This is a retrospective cohort study conducted among patients who underwent cardiac surgery under extracorporeal circulation during the year 2018 at the Cardiovascular Surgery Department of La Rabta University Hospital in Tunisia. Data were collected from the patients hospitalization records. The discrimination of the score was assessed using the ROC curve and the calibration using the Hosmer-Lemeshow goodness of fit test and then by constructing the calibration curve. Overall correct classification was also obtained. Results In our study, the observed mortality rate was 10.52% among the 95 included patients. The discriminating power of the CASUS score was estimated by the area under the ROC curve (AUC), this scoring system had a good discrimination with AUC greater than 0.9 from postoperative Day 0 to Day 5.From postoperative day 0 to day 5, the Hosmer-Lemeshow's test gave a value of chi square test statistic ranging from 1.474 to 8.42 and a value of level of significance ranging from 0.39 to 0.99 indicating a good calibration. The overall correct classification rate from postoperative day 0 to day 5 ranged from 84.4% to 92.4%. Conclusions Despite the differences in the profile of the risk factors between the Tunisian population and the population constituting the database used to develop the CASUS score, we can say that this risk model presents acceptable performances in our population, attested by adequate discrimination and calibration. Prospective and especially multicentre studies on larger samples are needed before definitively conclude on the performance of this model in our country. Key messages The casus score seems to be valid to predict mortality among patients undergoing cardiac surgery. Multicenter study on larger sample is needed to derive and validate models able to predict in-hospitals mortality.

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