scholarly journals Predictive Capacity of COVID-19 Test Positivity Rate

2021 ◽  
Author(s):  
Livio Fenga ◽  
Mauro Gaspari
Keyword(s):  
Intensive Care ◽  
Intensive Care Units ◽  
Moving Average ◽  
Analytical Framework ◽  
Daily Basis ◽  
Decision Makers ◽  
Alternative Formulation ◽  
Predictive Capacity ◽  
Number Of Patients ◽  
Analytical Tools

AbstractCOVID-19 infections can spread silently, due to the simultaneous presence of significant numbers of both critical and asymptomatic to mild cases. While for the former reliable data are available (in the form of number of hospitalization and/or beds in intensive care units), this is not the case of the latter. Hence, analytical tools designed to generate reliable forecast and future scenarios, should be implemented to help decision makers planning ahead (e.g. medical structures and equipment). Previous work of one of the authors shows that an alternative formulation of the Test Positivity Rate (TPR), i.e. the proportion of the number of persons tested positive in a given day, exhibits a strong correlation with the number of patients admitted in hospital and intensive care units. In this paper, we investigate the lagged correlation structure between the newly defined TPR and the hospitalized people time series, exploiting a rigorous statistical model, the Seasonal Auto Regressive Moving Average (SARIMA). The rigorous analytical framework chosen, i.e. the stochastic processes theory, allowed for a reliable forecasting about 12 days ahead, of those quantities. The proposed approach would also allow decision makers to forecast the number of beds in hospitals and intensive care units needed 12 days ahead. The obtained results show that a standardized TPR index is a valuable metric to monitor the growth of the COVID-19 epidemic. The index can be computed on daily basis and it is probably one of the best forecasting tools available today for predicting hospital and intensive care units overload, being an optimal compromise between simplicity of calculation and accuracy.

scholarly journals Predictive Capacity of COVID-19 Test Positivity Rate

2021 ◽  
Author(s):  
Livio Fenga ◽  
Mauro Gaspari
Keyword(s):  
Intensive Care ◽  
Intensive Care Units ◽  
Moving Average ◽  
Analytical Framework ◽  
Daily Basis ◽  
Decision Makers ◽  
Alternative Formulation ◽  
Predictive Capacity ◽  
Number Of Patients ◽  
Analytical Tools

Abstract COVID-19 infections can spread silently, due to the simultaneous presence of significant numbers of both critical and asymptomatic to mild cases. While for the former reliable data are available (in the form of number of hospitalization and/or beds in intensive care units), this is not the case of the latter. Hence, analytical tools designed to generate reliable forecast and future scenarios, should be implemented to help decision makers planning ahead (e.g. medical structures and equipment). Previous work of one of the authors shows that an alternative formulation of the Test Positivity Rate (TPR), i.e. the proportion of the number of persons tested positive in a given day, exhibits a strong correlation with the number of patients admitted in hospital and intensive care units. In this paper, we investigate the lagged correlation structure between the newly defined TPR and the hospitalized people time series, exploiting a rigorous statistical model, the Seasonal Auto Regressive Moving Average (SARIM A). The rigorous analytical framework chosen, i.e. the stochastic processes theory, allowed for a reliable forecasting about 12 days ahead, of those quantities. The proposed approach would also allow decision makers to forecast the number of beds in hospitals and intensive care units needed 12 days ahead. The obtained results show that a standardized TPR index is a valuable metric to monitor the growth of the COVID-19 epidemic. The index can be computed on daily basis and it is probably one of the best forecasting tools available today for predicting hospital and intensive care units overload, being an optimal compromise between simplicity of calculation and accuracy.

scholarly journals Predictive Capacity of COVID-19 Test Positivity Rate

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2435
Author(s):  
Livio Fenga ◽  
Mauro Gaspari
Keyword(s):  
Intensive Care ◽  
Intensive Care Units ◽  
Moving Average ◽  
Analytical Framework ◽  
Daily Basis ◽  
Decision Makers ◽  
Alternative Formulation ◽  
Predictive Capacity ◽  
Number Of Patients ◽  
Analytical Tools

COVID-19 infections can spread silently, due to the simultaneous presence of significant numbers of both critical and asymptomatic to mild cases. While, for the former reliable data are available (in the form of number of hospitalization and/or beds in intensive care units), this is not the case of the latter. Hence, analytical tools designed to generate reliable forecast and future scenarios, should be implemented to help decision-makers to plan ahead (e.g., medical structures and equipment). Previous work of one of the authors shows that an alternative formulation of the Test Positivity Rate (TPR), i.e., the proportion of the number of persons tested positive in a given day, exhibits a strong correlation with the number of patients admitted in hospitals and intensive care units. In this paper, we investigate the lagged correlation structure between the newly defined TPR and the hospitalized people time series, exploiting a rigorous statistical model, the Seasonal Auto Regressive Moving Average (SARIMA). The rigorous analytical framework chosen, i.e., the stochastic processes theory, allowed for a reliable forecasting about 12 days ahead of those quantities. The proposed approach would also allow decision-makers to forecast the number of beds in hospitals and intensive care units needed 12 days ahead. The obtained results show that a standardized TPR index is a valuable metric to monitor the growth of the COVID-19 epidemic. The index can be computed on daily basis and it is probably one of the best forecasting tools available today for predicting hospital and intensive care units overload, being an optimal compromise between simplicity of calculation and accuracy.

PREVALENCE OF CARBAPENEM-RESISTANT ACINETOBACTER BAUMANNII (CRAB) IN MEDICAL AND SURGICAL INTENSIVE CARE UNITS (ICUS) OF JPMC, KARACHI

2019 ◽  
Author(s):  
Rabia Arshad
Keyword(s):  
Intensive Care ◽  
Antimicrobial Resistance ◽  
Acinetobacter Baumannii ◽  
Intensive Care Units ◽  
Carbapenem Resistance ◽  
Chronic Obstructive ◽  
Surgical Intensive Care ◽  
Carbapenem Resistant ◽  
Number Of Patients ◽  
Increased Risk

Background: Antimicrobial resistance is one of the research priorities of health organizations due to increased risk of morbidity and mortality. Outbreaks of nosocomial infections caused by carbapenem-resistant Acinetobacter Baumannii (CRAB) strains are at rise worldwide. Antimicrobial resistance to carbapenems reduces clinical therapeutic choices and frequently led to treatment failure. The aim of our study was to determine the prevalence of carbapenem resistance in A. baumannii isolated from patients in intensive care units (ICUs). Methods: This cross-sectional study was carried out in the Department of Microbiology, Basic Medical Sciences Institute (BMSI), Jinnah Postgraduate Medical Centre (JPMC), Karachi, from December 2016 to November 2017. Total 63 non-repetitive A. baumannii were collected from the patients’ specimens, admitted to medical and surgical ICUs and wards of JPMC, Karachi. The bacterial isolates were processed according to standard microbiological procedures to observe for carbapenem resistance. SPSS 21 was used for data analysis. Results: Out of the 63 patients, 40 (63.5%) were male. The age of the patient ranged from 15-85 year, with average of 43 year. 34.9% patients had been hospitalized for 3 days. Chronic obstructive pulmonary disease was present in highest number with average of 58.7% for morbidity. Number of patients on mechanical ventilation was highest (65.1%). All isolates were susceptible to colistin. The resistance to ampicillin-sulbactam, ceftazidime, ciprofloxacin, amikacin, piperacillin- tazobactam and meropenem was 82.5%, 81%, 100%, 87.3%, 82.5% and 82% respectively. Out of 82% CRAB, 77% were obtained from ICUs. Conclusion: This study has revealed the high rate of carbapenem resistance in A. baumannii isolates in ICUs thus leaving behind limited therapeutic options.

scholarly journals Protocol and Fidelity Monitoring Plan for Four Supports. A Multicenter Trial of an Intervention to Support Surrogate Decision Makers in Intensive Care Units

2018 ◽  
Vol 15 (9) ◽  
pp. 1083-1091 ◽  
Author(s):  
Jennifer B. Seaman ◽  
Robert M. Arnold ◽  
Praewpannarai Buddadhumaruk ◽  
Anne-Marie Shields ◽  
Rachel M. Gustafson ◽  
...  
Keyword(s):  
Intensive Care ◽  
Intensive Care Units ◽  
Multicenter Trial ◽  
Decision Makers ◽  
Monitoring Plan ◽  
Fidelity Monitoring ◽  
Surrogate Decision Makers ◽  
Surrogate Decision

scholarly journals Level of acuity, severity and intensity of care of adults and older adults admitted to the Intensive Care Unit

2019 ◽  
Vol 53 ◽  
Author(s):  
Renata Eloah de Lucena Ferretti-Rebustini ◽  
Nilmar da Silva Bispo ◽  
Winnie da Silva Alves ◽  
Thiago Negreiro Dias ◽  
Cristiane Moretto Santoro ◽  
...  
Keyword(s):  
Older Adults ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Group Method ◽  
Predictive Capacity ◽  
Organic Dysfunction ◽  
Intensity Of Care ◽  
Correlation Tests ◽  
The City

ABSTRACT Objective: To characterize the level of acuity, severity and intensity of care of adults and older adults admitted to Intensive Care Units and to identify the predictors of severity with their respective predictive capacity according to the age group. Method: A retrospective cohort based on the analysis of medical records of individuals admitted to eight adult intensive care units in the city of São Paulo. The clinical characteristics at admission in relation to severity profile and intensity of care were analyzed through association and correlation tests. The predictors were identified by linear regression and the predictive capacity through the ROC curve. Results: Of the 781 cases (41.1% from older adults), 56.2% were males with a mean age of 54.1 ± 17.3 years. The burden of the disease, the organic dysfunction and the number of devices were the predictors associated with greater severity among adults and older adults, in which the organic dysfunction had the highest predictive capacity (80%) in both groups. Conclusion: Adults and older adults presented a similar profile of severity and intensity of care in admission to the Intensive Care Unit. Organic dysfunction was the factor with the best ability to predict severity in adults and older adults.

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