scholarly journals COVID-19 Time of Intubation Mortality Evaluation (C-TIME): A System for Predicting Mortality of Patients with COVID-19 Pneumonia at the Time They Require Mechanical Ventilation.

2022 ◽  
Author(s):  
Robert A Raschke ◽  
Pooja Rangan ◽  
Sumit Agarwal ◽  
Suresh Uppalapu ◽  
Nehan Sher ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Native American ◽  
Goodness Of Fit ◽  
Validation Cohort ◽  
Factor Xa ◽  
Mortality Prediction ◽  
Outcome Variable ◽  
Inform Consent ◽  
Family Expectations ◽  
On Line

Background: An accurate system to predict mortality in patients requiring intubation for COVID-19 could help to inform consent, frame family expectations and assist end-of-life decisions. Research objective: To develop and validate a mortality prediction system called C-TIME (COVID-19 Time of Intubation Mortality Evaluation) using variables available before intubation, determine its discriminant accuracy, and compare it to APACHE IVa and SOFA. Methods: A retrospective cohort was set in 18 medical-surgical ICUs, enrolling consecutive adults, positive by SARS-CoV 2 RNA by reverse transcriptase polymerase chain reaction or positive rapid antigen test, and undergoing endotracheal intubation. All were followed until hospital discharge or death. The combined outcome was hospital mortality or terminal extubation with hospice discharge. Twenty-five clinical and laboratory variables available 48 hours prior to intubation were entered into multiple logistic regression (MLR) and the resulting model was used to predict mortality of validation cohort patients. AUROC was calculated for C-TIME, APACHE IVa and SOFA. Results: The median age of the 2,440 study patients was 66 years; 61.6 percent were men, and 50.5 percent were Hispanic, Native American or African American. Age, gender, COPD, minimum mean arterial pressure, Glasgow Coma scale score, and PaO2/FiO2 ratio, maximum creatinine and bilirubin, receiving factor Xa inhibitors, days receiving non-invasive respiratory support and days receiving corticosteroids prior to intubation were significantly associated with the outcome variable. The validation cohort comprised 1,179 patients. C-TIME had the highest AUROC of 0.75 (95%CI 0.72-0.79), vs 0.67 (0.64-0.71) and 0.59 (0.55-0.62) for APACHE and SOFA, respectively (Chi2 P<0.0001). Conclusions: C-TIME is the only mortality prediction score specifically developed and validated for COVID-19 patients who require mechanical ventilation. It has acceptable discriminant accuracy and goodness-of-fit to assist decision-making just prior to intubation. The C-TIME mortality prediction calculator can be freely accessed on-line at https://phoenixmed.arizona.edu/ctime.

scholarly journals Magnitude and Associated Factors of Unintended Pregnancy among Pregnant Women at Saesie Tsaeda Emba Woreda Eastern Zone of Tigray, North Ethiopia, 2018

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Gebrehiwot Gebremariam Weldearegawi ◽  
Kidanemaryam Berhe Tekola ◽  
Berhane Fseha Teklehaymanot
Keyword(s):  
Family Planning ◽  
Pregnant Women ◽  
Unintended Pregnancy ◽  
Associated Factors ◽  
Goodness Of Fit ◽  
Outcome Variable ◽  
Significant Variable ◽  
Health Extension Workers ◽  
District Health ◽  
Variable Model

Background. Each year there were about 80 million women who experienced unintended pregnancy in the globe. In Ethiopia, around one third of women have experiences of unintended pregnancy. However, the magnitude of unintended pregnancy was not determined in the study area. Hence the aim of the study was to assess the magnitude and associated factors of unintended pregnancy among pregnant women. Methods. Institutional based cross-sectional study design was employed among 345 participants. Participants were selected by systematic random sampling. Data was collected though face to face interview by structured questioner. It was entered, clean and analyzed using SPSS version 20. Descriptive analysis was done to see the frequency, percentage, mean and standard deviation. Adjusted odds ratio was computed at 95% confidence level to determine the effect of independent variable on the outcome variable. Variable at p value < 0.05 was declared as statistically significant variable. Model goodness of fit was checked using Hosmer lemeshow test. Result. The overall magnitude of unintended pregnancy was 24.9%. Employed women were 60% less likely having unintended pregnancy (AOR 0.4, 95% CI: 0.015, 0.785).Single women were 1.4 times more likely reported unintended pregnancy (AOR 1.4, 95% CI: 1.005, 3.675). Unintended pregnancy among ever visited by health extension workers was 1.7 times higher than not visited (AOR 1.7, 95% CI: 1.09, 5. 128). Unintended pregnancy among who had information about family planning were about 70% less likely reported unintended pregnancy than their counterparties (AOR 0.3, 95% CI: 0 .067, 0.845). Marital status, occupational status, visited by health extension workers, having information about family planning, discussing with their partners about contraceptive were found the major factors of unintended pregnancy. Thus the district health office, Tigray regional health office and other stakeholder should work to improve family planning accessibility, awareness, and utilization to overcome the problem.

scholarly journals Development and Validation of a Mortality Prediction Model for Patients Receiving 14 Days of Mechanical Ventilation

2016 ◽  
Vol 60 (5) ◽  
pp. 185-186
Author(s):  
Catherine L. Hough ◽  
Ellen S. Caldwell ◽  
Christopher E. Cox ◽  
Ivor S. Douglas ◽  
Jeremy M. Kahn ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Prediction Model ◽  
Mortality Prediction ◽  
Mortality Prediction Model ◽  
Development And Validation

scholarly journals A Predictive Model of Live Birth Based on Obesity and Metabolic Parameters in Patients With PCOS Undergoing Frozen-Thawed Embryo Transfer

2022 ◽  
Vol 12 ◽  
Author(s):  
Xiaohua Jiang ◽  
Ruijun Liu ◽  
Ting Liao ◽  
Ye He ◽  
Caihua Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Embryo Transfer ◽  
Live Birth ◽  
Goodness Of Fit ◽  
Validation Cohort ◽  
Advanced Age ◽  
Training Cohort ◽  
Frozen Thawed Embryo Transfer ◽  
Thawed Embryo Transfer

AimsTo determine the clinical predictors of live birth in women with polycystic ovary syndrome (PCOS) undergoing frozen-thawed embryo transfer (F-ET), and to determine whether these parameters can be used to develop a clinical nomogram model capable of predicting live birth outcomes for these women.MethodsIn total, 1158 PCOS patients that were clinically pregnant following F-ET treatment were retrospectively enrolled in this study and randomly divided into the training cohort (n = 928) and the validation cohort (n = 230) at an 8:2 ratio. Relevant risk factors were selected via a logistic regression analysis approach based on the data from patients in the training cohort, and odds ratios (ORs) were calculated. A nomogram was constructed based on relevant risk factors, and its performance was assessed based on its calibration and discriminative ability.ResultsIn total, 20 variables were analyzed in the present study, of which five were found to be independently associated with the odds of live birth in univariate and multivariate logistic regression analyses, including advanced age, obesity, total cholesterol (TC), triglycerides (TG), and insulin resistance (IR). Having advanced age (OR:0.499, 95% confidence interval [CI]: 0.257 – 967), being obese (OR:0.506, 95% CI: 0.306 - 0.837), having higher TC levels (OR: 0.528, 95% CI: 0.423 - 0.660), having higher TG levels (OR: 0.585, 95% CI: 0.465 - 737), and exhibiting IR (OR:0.611, 95% CI: 0.416 - 0.896) were all independently associated with a reduced chance of achieving a live birth. A predictive nomogram incorporating these five variables was found to be well-calibrated and to exhibit good discriminatory capabilities, with an area under the curve (AUC) for the training group of 0.750 (95% CI, 0.709 - 0.788). In the independent validation cohort, this model also exhibited satisfactory goodness-of-fit and discriminative capabilities, with an AUC of 0.708 (95% CI, 0.615 - 0.781).ConclusionsThe nomogram developed in this study may be of value as a tool for predicting the odds of live birth for PCOS patients undergoing F-ET, and has the potential to improve the efficiency of pre-transfer management.

scholarly journals Regional pleural strain measurements during mechanical ventilation using ultrasound elastography: A randomised, crossover, proof of concept physiologic study

2022 ◽  
Author(s):  
Martin Girard ◽  
Marie-Hélène Roy Cardinal ◽  
Michaël Chassé ◽  
Sébastien Garneau ◽  
Yiorgos Alexandros Cavayas ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Goodness Of Fit ◽  
Tertiary Care ◽  
Intraclass Correlation ◽  
Ultrasound Elastography ◽  
Coefficient Of Determination ◽  
Proof Of Concept ◽  
Reliability Measures ◽  
Mixed Effect ◽  
Point Estimates

Background Mechanical ventilation is a common therapy in operating rooms and intensive care units. When ill-adapted, it can lead to ventilator-induced lung injury (VILI), which is associated with poor outcomes. Excessive regional pulmonary strain is thought to be a major mechanism responsible for VILI. Scarce bedside methods exist to measure regional pulmonary strain. We propose a novel way to measure regional pleural strain using ultrasound elastography. Research Question The objective of this study was to assess the feasibility and reliability of pleural strain measurement by ultrasound elastography and to determine if elastography parameters would correlate with varying tidal volumes. Study Design and Methods A single-blind randomized crossover proof of concept study was conducted July to October 2017 at a tertiary care referral center. Ten patients requiring general anesthesia for elective surgery were recruited. After induction, patients were received tidal volumes of 6, 8, 10 and 12 mL.kg-1 in random order, while pleural ultrasound cineloops were acquired at 4 standardized locations. Ultrasound radiofrequency speckle tracking allowed computing various pleural translation, strain and shear components. These were screened to identify those with the best dose-response with tidal volumes using linear mixed effect models. Goodness-of-fit was assessed by the coefficient of determination. Intraobserver, interobserver and test-retest reliability were calculated using intraclass correlation coefficients. Results Analysis was possible in 90.7% of ultrasound cineloops. Lateral absolute shear, lateral absolute strain and Von Mises strain varied significantly with tidal volume and offered the best dose-responses and data modelling fits. Point estimates for intraobserver reliability measures were excellent for all 3 parameters (0.94, 0.94 and 0.93, respectively). Point estimates for interobserver (0.84, 0.83 and 0.77, respectively) and test-retest (0.85, 0.82 and 0.76, respectively) reliability measures were good. Interpretation Strain imaging is feasible and reproducible, and may eventually guide mechanical ventilation strategies in larger cohorts of patients.

scholarly journals COVID-19 pneumonia in Galicia (Spain): Impact of prognostic factors and therapies on mortality and need for mechanical ventilation

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253465
Author(s):  
Luis Pérez-de-Llano ◽  
Eva María Romay-Lema ◽  
Adolfo Baloira-Villar ◽  
Christian Anchorena ◽  
María Luisa Torres-Durán ◽  
...  
Keyword(s):  
Risk Factors ◽  
Mechanical Ventilation ◽  
Logistic Regression ◽  
Therapeutic Interventions ◽  
Outcome Variable ◽  
Multivariable Logistic Regression Analysis ◽  
Composite Outcome ◽  
Multicenter Observational Study ◽  
Risk Of Death ◽  
Hospital Beds

Introduction This study was aimed to identify risk factors associated with unfavorable outcomes (composite outcome variable: mortality and need for mechanical ventilation) in patients hospitalized in Galicia with COVID-19 pneumonia. Methods Retrospective, multicenter, observational study carried out in the 8 Galician tertiary hospitals. All Patients admitted with confirmed COVID-19 pneumonia from 1st of March to April 24th, 2020 were included. A multivariable logistic regression analysis was performed in order to identify the relationship between risk factors, therapeutic interventions and the composite outcome variable. Results A total of 1292 patients (56.1% male) were included. Two hundred and twenty-five (17.4%) died and 327 (25.3%) reached the main outcome variable. Age [odds ratio (OR) = 1.03 (95% confidence interval (CI): 1.01–1.04)], CRP quartiles 3 and 4 [OR = 2.24 (95% CI: 1.39–3.63)] and [OR = 3.04 (95% CI: 1.88–4.92)], respectively, Charlson index [OR = 1.16 (95%CI: 1.06–1.26)], SaO2 upon admission [OR = 0.93 (95% CI: 0.91–0.95)], hydroxychloroquine prescription [OR = 0.22 (95%CI: 0.12–0.37)], systemic corticosteroids prescription [OR = 1.99 (95%CI: 1.45–2.75)], and tocilizumab prescription [OR = 3.39 (95%CI: 2.15–5.36)], significantly impacted the outcome. Sensitivity analysis using different alternative logistic regression models identified consistently the ratio admissions/hospital beds as a predictor of the outcome [OR = 1.06 (95% CI: 1.02–1.11)]. Conclusion These findings may help to identify patients at hospital admission with a higher risk of death and may urge healthcare authorities to implement policies aimed at reducing deaths by increasing the availability of hospital beds.

scholarly journals Use of a Mortality Prediction Model in Children on Mechanical Ventilation: A 5-Year Experience in a Tertiary University Hospital

2020 ◽  
Vol Volume 13 ◽  
pp. 1507-1516
Author(s):  
Waleed H Albuali ◽  
Amal A Algamdi ◽  
Elham A Hasan ◽  
Mohammad H Al-Qahtani ◽  
Abdullah A Yousef ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Prediction Model ◽  
Mortality Prediction ◽  
University Hospital ◽  
Mortality Prediction Model

scholarly journals Development and Validation of a Risk Tool for Predicting Severe Toxicity in Older Adults Receiving Chemotherapy for Early-Stage Breast Cancer

2021 ◽  
Vol 39 (6) ◽  
pp. 608-618 ◽  
Author(s):  
Allison Magnuson ◽  
Mina S. Sedrak ◽  
Cary P. Gross ◽  
William P. Tew ◽  
Heidi D. Klepin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Older Adults ◽  
Goodness Of Fit ◽  
Validation Cohort ◽  
Early Stage ◽  
Risk Groups ◽  
Early Stage Breast Cancer ◽  
Severe Toxicity ◽  
Chemotherapy Toxicity ◽  
Grade 3

PURPOSE Limited tools exist to predict the risk of chemotherapy toxicity in older adults with early-stage breast cancer. METHODS Patients of age ≥ 65 years with stage I-III breast cancer from 16 institutions treated with neoadjuvant or adjuvant chemotherapy were prospectively evaluated for geriatric and clinical features predictive of grade 3-5 chemotherapy toxicity. Logistic regression with best-subsets selection was used to identify and incorporate independent predictors of toxicity into a model with weighted variable scoring. Model performance was evaluated using area under the ROC curve (AUC) and goodness-of-fit statistics. The model was internally and externally validated. RESULTS In 473 patients (283 in development and 190 in validation cohort), 46% developed grade 3-5 chemotherapy toxicities. Eight independent predictors were identified (each assigned weighted points): anthracycline use (1 point), stage II or III (3 points), planned treatment duration > 3 months (4 points), abnormal liver function (3 points), low hemoglobin (3 points), falls (4 points), limited walking (3 points), and lack of social support (3 points). We calculated risk scores for each patient and defined three risk groups: low (0-5 points), intermediate (6-11 points), or high (≥ 12 points). In the development cohort, the rates of grade 3-5 chemotherapy toxicity for these three groups were 19%, 54%, and 87%, respectively ( P < .01). In the validation cohort, the corresponding toxicity rates were 27%, 45%, and 76%. The AUC was 0.75 (95% CI, 0.70 to 0.81) in the development cohort and 0.69 (95% CI, 0.62 to 0.77) in the validation cohort. Risk groups were also associated with hospitalizations and reduced dose intensity ( P < .01). CONCLUSION The Cancer and Aging Research Group-Breast Cancer (CARG-BC) score was developed and validated to predict grade 3-5 chemotherapy toxicity in older adults with early-stage breast cancer.

scholarly journals Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19

2020 ◽  
pp. bmjspcare-2020-002602 ◽  
Author(s):  
Prathamesh Parchure ◽  
Himanshu Joshi ◽  
Kavita Dharmarajan ◽  
Robert Freeman ◽  
David L Reich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hospital Mortality ◽  
Characteristic Curve ◽  
Laboratory Data ◽  
Mortality Prediction ◽  
Outcome Variable ◽  
Electronic Health Record Data ◽  
Test Set ◽  
Electronic Health ◽  
Near Term

ObjectivesTo develop and validate a model for prediction of near-term in-hospital mortality among patients with COVID-19 by application of a machine learning (ML) algorithm on time-series inpatient data from electronic health records.MethodsA cohort comprised of 567 patients with COVID-19 at a large acute care healthcare system between 10 February 2020 and 7 April 2020 observed until either death or discharge. Random forest (RF) model was developed on randomly drawn 70% of the cohort (training set) and its performance was evaluated on the rest of 30% (the test set). The outcome variable was in-hospital mortality within 20–84 hours from the time of prediction. Input features included patients’ vital signs, laboratory data and ECG results.ResultsPatients had a median age of 60.2 years (IQR 26.2 years); 54.1% were men. In-hospital mortality rate was 17.0% and overall median time to death was 6.5 days (range 1.3–23.0 days). In the test set, the RF classifier yielded a sensitivity of 87.8% (95% CI: 78.2% to 94.3%), specificity of 60.6% (95% CI: 55.2% to 65.8%), accuracy of 65.5% (95% CI: 60.7% to 70.0%), area under the receiver operating characteristic curve of 85.5% (95% CI: 80.8% to 90.2%) and area under the precision recall curve of 64.4% (95% CI: 53.5% to 75.3%).ConclusionsOur ML-based approach can be used to analyse electronic health record data and reliably predict near-term mortality prediction. Using such a model in hospitals could help improve care, thereby better aligning clinical decisions with prognosis in critically ill patients with COVID-19.

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