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10.1016/j.cmi.2020.08.003 http://scihub22266oqcxt.onion/10.1016/j.cmi.2020.08.003 32781244!7414420!32781244     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 300.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 300.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Clin+Microbiol+Infect 2020 ; 26 (11): 1545-1553 Nephropedia Template TP {{tp|p=32781244|t=2020. Development and validation of a prediction model for severe respiratory failure in hospitalized patients with SARS-CoV-2 infection: a multicentre cohort study (PREDI-CO study) |pdf=|usr=}}{{32781244}} gab.com Text Development and validation of a prediction model for severe respiratory failure in hospitalized patients with SARS-CoV-2 infection: a multicentre cohort study (PREDI-CO study) JO: Clin Microbiol Infect http://www.kidney.de/pget.php?&tw=1&pmid=32781244 #FOAvip OBJECTIVES: We aimed to develop and validate a risk score to predict severe respiratory failure (SRF) among patients hospitalized with coronavirus disease-2019 (COVID-19). METHODS: We performed a multicentre cohort study among hospitalized (>24 hours) patients diagnosed with COVID-19 from 22 February to 3 April 2020, at 11 Italian hospitals. Patients were divided into derivation and validation cohorts according to random sorting of hospitals. SRF was assessed from admission to hospital discharge and was defined as: Spo(2) <93% with 100% Fio(2), respiratory rate >30 breaths/min or respiratory distress. Multivariable logistic regression models were built to identify predictors of SRF, beta-coefficients were used to develop a risk score. Trial Registration NCT04316949. RESULTS: We analysed 1113 patients (644 derivation, 469 validation cohort). Mean (+/-SD) age was 65.7 (+/-15) years, 704 (63.3%) were male. SRF occurred in 189/644 (29%) and 187/469 (40%) patients in the derivation and validation cohorts, respectively. At multivariate analysis, risk factors for SRF in the derivation cohort assessed at hospitalization were age >/=70 years (OR 2.74; 95% CI 1.66-4.50), obesity (OR 4.62; 95% CI 2.78-7.70), body temperature >/=38 degrees C (OR 1.73; 95% CI 1.30-2.29), respiratory rate >/=22 breaths/min (OR 3.75; 95% CI 2.01-7.01), lymphocytes </=900 cells/mm(3) (OR 2.69; 95% CI 1.60-4.51), creatinine >/=1 mg/dL (OR 2.38; 95% CI 1.59-3.56), C-reactive protein >/=10 mg/dL (OR 5.91; 95% CI 4.88-7.17) and lactate dehydrogenase >/=350 IU/L (OR 2.39; 95% CI 1.11-5.11). Assigning points to each variable, an individual risk score (PREDI-CO score) was obtained. Area under the receiver-operator curve was 0.89 (0.86-0.92). At a score of >3, sensitivity, specificity, and positive and negative predictive values were 71.6% (65%-79%), 89.1% (86%-92%), 74% (67%-80%) and 89% (85%-91%), respectively. PREDI-CO score showed similar prognostic ability in the validation cohort: area under the receiver-operator curve 0.85 (0.81-0.88). At a score of >3, sensitivity, specificity, and positive and negative predictive values were 80% (73%-85%), 76% (70%-81%), 69% (60%-74%) and 85% (80%-89%), respectively. CONCLUSION: PREDI-CO score can be useful to allocate resources and prioritize treatments during the COVID-19 pandemic. Twit Text FOAVip Development and validation of a prediction model for severe respiratory failure in hospitalized patients with SARS-CoV-2 infection: a multicentre cohort study (PREDI-CO study) ????Clin Microbiol Infect http://www.kidney.de/pget.php?&tw=1&pmid=32781244 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32781244 #FOAvip English Wikipedia <ref>{{Cite pmid|32781244}}</ref> Development and validation of a prediction model for severe respiratory failure in hospitalized patients with SARS-CoV-2 infection: a multicentre cohort study (PREDI-CO study) #MMPMID32781244 Bartoletti M; Giannella M; Scudeller L; Tedeschi S; Rinaldi M; Bussini L; Fornaro G; Pascale R; Pancaldi L; Pasquini Z; Trapani F; Badia L; Campoli C; Tadolini M; Attard L; Puoti M; Merli M; Mussini C; Menozzi M; Meschiari M; Codeluppi M; Barchiesi F; Cristini F; Saracino A; Licci A; Rapuano S; Tonetti T; Gaibani P; Ranieri VM; Viale P Clin Microbiol Infect 2020[Nov]; 26 (11): 1545-1553 PMID32781244show ga OBJECTIVES: We aimed to develop and validate a risk score to predict severe respiratory failure (SRF) among patients hospitalized with coronavirus disease-2019 (COVID-19). METHODS: We performed a multicentre cohort study among hospitalized (>24 hours) patients diagnosed with COVID-19 from 22 February to 3 April 2020, at 11 Italian hospitals. Patients were divided into derivation and validation cohorts according to random sorting of hospitals. SRF was assessed from admission to hospital discharge and was defined as: Spo(2) <93% with 100% Fio(2), respiratory rate >30 breaths/min or respiratory distress. Multivariable logistic regression models were built to identify predictors of SRF, beta-coefficients were used to develop a risk score. Trial Registration NCT04316949. RESULTS: We analysed 1113 patients (644 derivation, 469 validation cohort). Mean (+/-SD) age was 65.7 (+/-15) years, 704 (63.3%) were male. SRF occurred in 189/644 (29%) and 187/469 (40%) patients in the derivation and validation cohorts, respectively. At multivariate analysis, risk factors for SRF in the derivation cohort assessed at hospitalization were age >/=70 years (OR 2.74; 95% CI 1.66-4.50), obesity (OR 4.62; 95% CI 2.78-7.70), body temperature >/=38 degrees C (OR 1.73; 95% CI 1.30-2.29), respiratory rate >/=22 breaths/min (OR 3.75; 95% CI 2.01-7.01), lymphocytes /=1 mg/dL (OR 2.38; 95% CI 1.59-3.56), C-reactive protein >/=10 mg/dL (OR 5.91; 95% CI 4.88-7.17) and lactate dehydrogenase >/=350 IU/L (OR 2.39; 95% CI 1.11-5.11). Assigning points to each variable, an individual risk score (PREDI-CO score) was obtained. Area under the receiver-operator curve was 0.89 (0.86-0.92). At a score of >3, sensitivity, specificity, and positive and negative predictive values were 71.6% (65%-79%), 89.1% (86%-92%), 74% (67%-80%) and 89% (85%-91%), respectively. PREDI-CO score showed similar prognostic ability in the validation cohort: area under the receiver-operator curve 0.85 (0.81-0.88). At a score of >3, sensitivity, specificity, and positive and negative predictive values were 80% (73%-85%), 76% (70%-81%), 69% (60%-74%) and 85% (80%-89%), respectively. CONCLUSION: PREDI-CO score can be useful to allocate resources and prioritize treatments during the COVID-19 pandemic. |*Logistic Models[MESH] |Adolescent[MESH] |Adult[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Child[MESH] |Child, Preschool[MESH] |Coronavirus Infections/*diagnosis/epidemiology[MESH] |Female[MESH] |Hospitalization[MESH] |Humans[MESH] |Italy/epidemiology[MESH] |Male[MESH] |Middle Aged[MESH] |Multivariate Analysis[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnosis/epidemiology[MESH] |Prognosis[MESH] |Reproducibility of Results[MESH] |Respiratory Insufficiency/*diagnosis/epidemiology[MESH] |Retrospective Studies[MESH] |Risk Assessment[MESH] |Risk Factors[MESH] |SARS-CoV-2[MESH] |Sensitivity and Specificity[MESH]Development and validation of a prediction model for severe respirator(epmc).pdf DeepDyve Pubget Overpricing