Development and validation of nomogram to predict severe illness requiring intensive care follow up in hospitalized COVID-19 cases

Abstract Background Early identification of severe COVID-19 patients who will need intensive care unit (ICU) follow-up and providing rapid, aggressive supportive care may reduce mortality and provide optimal use of medical resources. We aimed to develop and validate a nomogram to predict severe COVID-19 cases that would need ICU follow-up based on available and accessible patient values. Methods Patients hospitalized with laboratory-confirmed COVID-19 between March 15, 2020, and June 15, 2020, were enrolled in this retrospective study with 35 variables obtained upon admission considered. Univariate and multivariable logistic regression models were constructed to select potential predictive parameters using 1000 bootstrap samples. Afterward, a nomogram was developed with 5 variables selected from multivariable analysis. The nomogram model was evaluated by Area Under the Curve (AUC) and bias-corrected Harrell's C-index with 95% confidence interval, Hosmer–Lemeshow Goodness-of-fit test, and calibration curve analysis. Results Out of a total of 1022 patients, 686 cases without missing data were used to construct the nomogram. Of the 686, 104 needed ICU follow-up. The final model includes oxygen saturation, CRP, PCT, LDH, troponin as independent factors for the prediction of need for ICU admission. The model has good predictive power with an AUC of 0.93 (0.902–0.950) and a bias-corrected Harrell's C-index of 0.91 (0.899–0.947). Hosmer–Lemeshow test p-value was 0.826 and the model is well-calibrated (p = 0.1703). Conclusion We developed a simple, accessible, easy-to-use nomogram with good distinctive power for severe illness requiring ICU follow-up. Clinicians can easily predict the course of COVID-19 and decide the procedure and facility of further follow-up by using clinical and laboratory values of patients available upon admission.

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Predictive value of Score for Neonatal Acute Physiology and Perinatal Extension II for neonatal mortality in Sanglah Hospital, Denpasar, Indonesia

Paediatrica Indonesiana ◽

10.14238/pi56.5.2016.257-61 ◽

2017 ◽

Vol 56 (5) ◽

pp. 257

Author(s):

I Gede Ketut Aryana ◽

I Made Kardana ◽

I Nyoman Adipura

Keyword(s):

Intensive Care ◽

Neonatal Mortality ◽

Goodness Of Fit ◽

Assessment Tool ◽

Area Under The Curve ◽

Severe Infection ◽

Scoring Systems ◽

World Health ◽

Severe Illness ◽

Goodness Of Fit Test

Background Neonatal mortality, which is largely caused by severe illness, is the biggest contributor to overall infant mortality. The World Health Organization (WHO) estimated that 4 million neonates die yearly worldwide, often due to severe infection and organ system immaturity. Neonates with severe illness require treatment in the neonatal intensive care unit (NICU), in which a reliable assessment tool for illness severity is needed to guide intensive care requirements and prognosis. Neonatal disease severity scoring systems have been developed, including Score for Neonatal Acute Physiology and Perinatal Extension II (SNAPPE II), but it has never been validated in our setting.ObjectiveTo study the prognostic value of SNAPPE II as a predictor of neonatal mortality in Sanglah Hospital, Denpasar, Indonesia.Methods This prospective cohort study was conducted in the NICU of Sanglah Hospital, Denpasar from November 2014 to February 2015. All neonates, except those with congenital anomaly, were observed during the first 12 hours of admission and their outcomes upon discharge from the NICU was recorded. We assessed the SNAPPE II cut-off point to predict neonatal mortality. The calibration of SNAPPE II was done using the Hosmer-Lemeshow goodness-of-fit test, and discrimination of SNAPPE II was determined from the receiver-operator characteristic (ROC) curve and area under the curve (AUC) value calculation.ResultsDuring the period of study, 63 children were eligible, but 5 were excluded because of major congenital abnormalities. The SNAPPE II optimum cut-off point of 37 gave a high probability of mortality and the ROC showed an AUC of 0.92 (95%CI 0.85 to 0.99). The Hosmer-Lemeshow goodness-of-fit test showed a good calibration with P = 1.0Conclusion The SNAPPE II has a good predictive ability for neonatal mortality in Sanglah Hospital, Denpasar, Indonesia.

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Ankara City Hospital COVID-19 Severity Score (ACCSES):A Calculation Tool in the Prediction of Severe Illness Requiring Intensive Care Follow-up

10.21203/rs.3.rs-63960/v1 ◽

2020 ◽

Author(s):

Rahmet Guner ◽

Bircan Kayaaslan ◽

Imran Hasanoglu ◽

Adalet Aypak ◽

Hurrem Bodur ◽

...

Keyword(s):

Intensive Care ◽

Prediction Model ◽

Severity Score ◽

Development Process ◽

Binary Logistic Regression ◽

Severe Illness ◽

City Hospital ◽

Predictive Parameters ◽

Predicted Probability

Abstract Aim: The aim of this study is to develop and explain an easy-to-use severity score calculation tool to predict severe COVID-19 cases that would need intensive care unit (ICU) follow-up.Material method: The study was carried out in patients with laboratory-confirmed COVID-19 hospitalized in Ankara City Hospital between March 15, 2020, and June 15, 2020. The outcome was severe illness that required ICU follow-up. Univariate and binary logistic regression were used to create a prediction model by using potential predictive parameters obtained on the day of hospitalization. Youden’s J index was calculated with receiver-operator characteristic curves analysis in order to evaluate cut-off points, and predicted probability was calculated. The accuracy of the prediction model was tested by calculating the area under curve (AUC). Results: Of the total of 1022 patients, 152 had a severe illness and required ICU follow-up. Among 68 variables, 20 parameters met the potential predictive factor condition for severe illness and were included in the development process for ANKARA CITY HOSPITAL COVID-19 SEVERITY SCORE (ACCSES). The ACCSES calculation tool was created by the final 9 parameters (sex, oxygen saturation, hemoglobin, platelet count, glomerular filtration rate, aspartate transaminase, procalcitonin, ferritin, and D-dimer). AUC was 0.96 (95% CI, 0.95-0.98).Conclusion: We developed a simple, accessible, easy to use calculation tool, ACCSES, with good distinctive power for a severe illness that required ICU follow-up. The clinician can easily predict the course of COVID-19 and decide the procedure and facility of further follow-up simply using available clinical and laboratory values of patients upon admission.

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The impact of SPY angiography on intraoperative decision making and outcomes for post-mastectomy reconstruction

Journal of Cancer Science and Therapeutics ◽

10.36879/jcst.19.000109 ◽

2019 ◽

pp. 1-5

Keyword(s):

Decision Making ◽

Logistic Regression ◽

Skin Necrosis ◽

Regression Models ◽

Multivariable Analysis ◽

P Value ◽

Flap Necrosis ◽

Single Institution ◽

Logistic Regression Models ◽

The Impact

Objective: While the use of intraoperative laser angiography (SPY) is increasing in mastectomy patients, its impact in the operating room to change the type of reconstruction performed has not been well described. The purpose of this study is to investigate whether SPY angiography influences post-mastectomy reconstruction decisions and outcomes. Methods and materials: A retrospective analysis of mastectomy patients with reconstruction at a single institution was performed from 2015-2017.All patients underwent intraoperative SPY after mastectomy but prior to reconstruction. SPY results were defined as ‘good’, ‘questionable’, ‘bad’, or ‘had skin excised’. Complications within 60 days of surgery were compared between those whose SPY results did not change the type of reconstruction done versus those who did. Preoperative and intraoperative variables were entered into multivariable logistic regression models if significant at the univariate level. A p-value <0.05 was considered significant. Results: 267 mastectomies were identified, 42 underwent a change in the type of planned reconstruction due to intraoperative SPY results. Of the 42 breasts that underwent a change in reconstruction, 6 had a ‘good’ SPY result, 10 ‘questionable’, 25 ‘bad’, and 2 ‘had areas excised’ (p<0.01). After multivariable analysis, predictors of skin necrosis included patients with ‘questionable’ SPY results (p<0.01, OR: 8.1, 95%CI: 2.06 – 32.2) and smokers (p<0.01, OR:5.7, 95%CI: 1.5 – 21.2). Predictors of any complication included a change in reconstruction (p<0.05, OR:4.5, 95%CI: 1.4-14.9) and ‘questionable’ SPY result (p<0.01, OR: 4.4, 95%CI: 1.6-14.9). Conclusion: SPY angiography results strongly influence intraoperative surgical decisions regarding the type of reconstruction performed. Patients most at risk for flap necrosis and complication post-mastectomy are those with questionable SPY results.

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Ethnic differences in the incidence of pterygium in a multi-ethnic Asian population: the Singapore Epidemiology of Eye Diseases Study

Scientific Reports ◽

10.1038/s41598-020-79920-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Xiao Ling Fang ◽

Crystal Chun Yuen Chong ◽

Sahil Thakur ◽

Zhi Da Soh ◽

Zhen Ling Teo ◽

...

Keyword(s):

Ethnic Differences ◽

Regression Models ◽

Multivariable Analysis ◽

Anterior Segment ◽

Asian Population ◽

Eye Diseases ◽

Logistic Regression Models ◽

Income Status ◽

Baseline Visit

AbstractWe evaluated the 6-year incidence and risk factors of pterygium in a multi-ethnic Asian population. Participants who attended the baseline visit of the Singapore Epidemiology of Eye Diseases Study (year 2004–2011) and returned six years later, were included in this study. Pterygium was diagnosed based on anterior segment photographs. Incident pterygium was defined as presence of pterygium at 6-year follow-up in either eye, among individuals without pterygium at baseline. Multivariable logistic regression models were used to determine factors associated with incident pterygium, adjusting for baseline age, gender, ethnicity, body mass index, occupation type, educational level, income status, smoking, alcohol consumption, presence of hypertension, diabetes and hyperlipidemia. The overall age-adjusted 6-year incidence of pterygium was 1.2% (95% confidence interval [CI] 1.0–1.6%); with Chinese (1.9%; 95% CI 1.4%-2.5%) having the highest incidence rate followed by Malays (1.4%; 95% CI 0.9%-2.1%) and Indians (0.3%; 95% CI 0.3–0.7%). In multivariable analysis, Chinese (compared with Indians; odds ratio [OR] = 4.21; 95% CI 2.12–9.35) and Malays (OR 3.22; 95% CI 1.52–7.45), male (OR 2.13; 95% CI 1.26–3.63), outdoor occupation (OR 2.33; 95% CI 1.16–4.38), and smoking (OR 0.41; 95% CI 0.16–0.87) were significantly associated with incident pterygium. Findings from this multi-ethnic Asian population provide useful information in identifying at-risk individuals for pterygium.

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External validation of the European risk assessment tool for cardio metabolic disease in a Middle Eastern population

10.21203/rs.3.rs-22990/v1 ◽

2020 ◽

Author(s):

Samaneh Asgari ◽

Fatemeh Moosaie ◽

Davood Khalili ◽

Fereidoun Azizi ◽

Farzad Hadaegh

Keyword(s):

Metabolic Disease ◽

Cardiovascular Disease ◽

Risk Assessment ◽

Goodness Of Fit ◽

Assessment Tool ◽

External Validation ◽

Iranian Population ◽

Risk Assessment Tool ◽

Goodness Of Fit Test

Abstract Background: High burden of chronic cardio-metabolic disease (CCD) including type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), and cardiovascular disease (CVD) have been reported in the Middle East and North Africa region. We aimed to externally validate a Europoid risk assessment tool designed by Alssema et al, including non-laboratory measures, for the prediction of the CCD in the Iranian population. Methods: The predictors included age, body mass index, waist circumference, use of antihypertensive, current smoking, and family history of cardiovascular disease and or diabetes. For external validation of the model in the Tehran lipids and glucose study (TLGS), the Area under the curve (AUC) and the Hosmer-Lemeshow (HL) goodness of fit test were performed for discrimination and calibration, respectively. Results: Among 1310 men and 1960 women aged 28-85 years, 29.5% and 47.4% experienced CCD during the 6 and 9-year follow-up, respectively. The model showed acceptable discrimination, with an AUC of 0.72(95% CI: 0.69-0.75) for men and 0.73(95% CI: 0.71-0.76) for women. The calibration of the model was good for both genders (min HL P=0.5). Considering separate outcomes, AUC was highest for CKD (0.76(95% CI: 0.72-0.79)) and lowest for T2DM (0.65(95% CI: 0.61-0.69)), in men. As for women, AUC was highest for CVD (0.82(95% CI: 0.78-0.86)) and lowest for T2DM (0.69(95% CI: 0.66-0.73)). The 9-year follow-up demonstrated almost similar performances compared to the 6-year follow-up. Conclusion: This model showed acceptable discrimination and good calibration for risk prediction of CCD in short and long-term follow-up in the Iranian population.

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Validity of Pediatric Index of Mortality 2 score as an Outcome Predictor in Pediatric ICU of a Public Sector Tertiary Care Hospital in Pakistan

Journal of Pediatric Intensive Care ◽

10.1055/s-0040-1722758 ◽

2021 ◽

Author(s):

Muhammad Bilal Mazhar ◽

Muhammad Haroon Hamid

Keyword(s):

Intensive Care ◽

Population Characteristics ◽

P Value ◽

Care Hospital ◽

Outcome Predictor ◽

Goodness Of Fit Test ◽

Chi Square ◽

Common Diagnosis ◽

Chi Square Analysis ◽

Pediatric Index Of Mortality

AbstractPediatric Index of Mortality 2 (PIM-2) is one of the leading mortality scores used in intensive care units all around the world. We assessed its validity as an outcome predictor in a pediatric intensive care unit (PICU) of Mayo Hospital/King Edward Medical University Lahore, Pakistan. We enrolled 154 consecutive admissions, aged 1 month to 13 years, requiring intensive care from January to June of 2019. Patient demographics along with PIM-2 data were collected; PIM-2 score and mortality risk was calculated; and the outcome recorded as death or survival. The median age at admission was 0.50 years (interquartile range [IQR]: 0.24–1.78) and the median weight was 5.0 kg (IQR: 3.08–10.0) with females constituting 54%; malnutrition was also common (66%). Observed mortality was 29.9% (46 out of 154) and expected mortality (cut-off ≥ 99.8%) was 27.9% with a standardized mortality ratio of 1.07 (95% confidence interval [CI]: 0.79–1.41). Sepsis was the most common diagnosis at admission (27.9%) with the highest mortality (52.2%). Chi-square analysis revealed a sensitivity of 54.3% and a specificity of 83.3% (p-value 0.00). PIM-2 score showed acceptable discrimination between survivors and nonsurvivors with an area under the receiver operating characteristic curve of 0.75 (95% CI: 0.67–0.84) (p-value = 0.00); however, poor calibration according to Hosmer–Lemeshow goodness of fit test (Chi-square = 15.80, df = 7, and p-value of 0.027 [< 0.1]), thus requiring recalibration according to local population characteristics.

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Is the EuroSCORE II reliable in surgical mitral valve repair? A single-centre validation study

European Journal of Cardio-Thoracic Surgery ◽

10.1093/ejcts/ezaa403 ◽

2020 ◽

Author(s):

Davide Carino ◽

Paolo Denti ◽

Guido Ascione ◽

Benedetto Del Forno ◽

Elisabetta Lapenna ◽

...

Keyword(s):

Mitral Valve ◽

Mitral Valve Repair ◽

Goodness Of Fit ◽

Area Under The Curve ◽

High Volume ◽

Brier Score ◽

Calibration Plot ◽

Valve Repair ◽

Goodness Of Fit Test ◽

High Level

Abstract OBJECTIVES The EuroSCORE II is widely used to predict 30-day mortality in patients undergoing open and transcatheter cardiac surgery. The aim of this study is to evaluate the discriminatory ability of the EuroSCORE II in predicting 30-day mortality in a large cohort of patients undergoing surgical mitral valve repair in a high-volume centre. METHODS A retrospective review of our institutional database was carried on to find all patients who underwent mitral valve repair in our department from January 2012 to December 2019. Discrimination of the EuroSCORE II was assessed using receiver operating characteristic curves. The maximum Youden’s Index was employed to define the optimal cut-point. Calibration was assessed by generating calibration plot that visually compares the predicted mortality with the observed mortality. Calibration was also tested with the Hosmer–Lemeshow goodness-of-fit test. Finally, the accuracy of the models was tested calculating the Brier score. RESULTS A total of 2645 patients were identified, and the median EuroSCORE II was 1.3% (0.6–2.0%). In patients with degenerative mitral regurgitation (MR), the EuroSCORE II showed low discrimination (area under the curve 0.68), low accuracy (Brier score 0.27) and low calibration with overestimation of the 30-day mortality. In patients with secondary MR, the EuroSCORE II showed a good overall performance estimating the 30-day mortality with good discrimination (area under the curve 0.88), good accuracy (Brier score 0.003) and good calibration. CONCLUSIONS In patients with degenerative MR operated on in a high-volume centre with a high level of expertise in mitral valve repair, the EuroSCORE II significantly overestimates the 30-day mortality.

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Variables Affecting Peri-Implant Radiographic Bone Loss-8-23 Years Follow-Up

Applied Sciences ◽

10.3390/app10238591 ◽

2020 ◽

Vol 10 (23) ◽

pp. 8591

Author(s):

Michael Saminsky ◽

Anat Ben Dor ◽

Jacob Horwitz

Keyword(s):

Bone Loss ◽

Goodness Of Fit ◽

Operating Characteristic ◽

Characteristic Curve ◽

Area Under The Curve ◽

Factors Associated ◽

Implant Placement ◽

Dental Records

The aim of this study is to evaluate factors associated with long-term peri-implant bone-loss and to create a statistical model explaining bone-loss. The dental records in a private periodontal practice were screened for implant-patients with a minimal follow-up period of 8 years with periapical radiographs at implant-placement (T0) and last follow-up (Tf). Collected data included demographics, general health, medications, periodontal parameters, implant parameters, bone augmentation procedures, restoration and antagonist data, number of supportive periodontal appointments (SPT), and radiographic bone-loss between T0 and Tf. Bivariate and Mixed Logistic Regression analyses were performed. “Goodness-of-fit” of the model was elaborated with Receiver Operating Characteristic Curve (ROC) analyses. Thirty-seven patients receiving 142 implants were included. Mean clinical follow-up period was 11.7 ± 3.7 years (range 8–23). Most implants 64.4% were SPT-maintained more than twice a year. Patients with osteoporosis and smokers were prone to increased radiographic peri-implant bone-loss. External-hex implants placed without guided bone regeneration (GBR) and implants 10–12 mm long and diameter of 3.7–4 mm showed less peri-implant bone-loss. The model’s Area Under the Curve (AUC) was 76.9% (Standard Error 4.6%, CI 67.8%–86%).

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Physical, Psychological, and Social Factors Associated with Exacerbation-Related Hospitalization in Patients with COPD

Journal of Clinical Medicine ◽

10.3390/jcm9030636 ◽

2020 ◽

Vol 9 (3) ◽

pp. 636

Author(s):

Mieke R.C. Crutsen ◽

Spencer J. Keene ◽

Daisy J.A. Janssen Nienke Nakken ◽

Miriam T. Groenen ◽

Sander M.J. van Kuijk ◽

...

Keyword(s):

Goodness Of Fit ◽

Univariate Analysis ◽

Gain Control ◽

Chronic Obstructive ◽

Goodness Of Fit Test ◽

Timed Up And Go ◽

Obstructive Pulmonary Disease ◽

Prospective Longitudinal Study ◽

Prospective Longitudinal

Background and objective: Exacerbation(s) of chronic obstructive pulmonary disease (eCOPD) entail important events describing an acute deterioration of respiratory symptoms. Changes in medication and/or hospitalization are needed to gain control over the event. However, an exacerbation leading to hospitalization is associated with a worse prognosis for the patient. The objective of this study is to explore factors that could predict the probability of an eCOPD-related hospitalization. Methods: Data from 128 patients with COPD included in a prospective, longitudinal study were used. At baseline, physical, emotional, and social status of the patients were assessed. Moreover, hospital admission during a one year follow-up was captured. Different models were made based on univariate analysis, literature, and practice. These models were combined to come to one final overall prediction model. Results: During follow-up, 31 (24.2%) participants were admitted for eCOPD. The overall model contained six significant variables: currently smoking (OR = 3.93), forced vital capacity (FVC; OR = 0.97), timed-up-and-go time (TUG-time) (OR = 14.16), knowledge (COPD knowledge questionnaire, percentage correctly answered questions (CIROPD%correct)) (<60% (OR = 1.00); 60%–75%: (OR = 0.30); >75%: (OR = 1.94), eCOPD history (OR = 9.98), and care dependency scale (CDS) total score (OR = 1.12). This model was well calibrated (goodness-of-fit test: p = 0.91) and correctly classified 79.7% of the patients. Conclusion: A combination of TUG-time, eCOPD-related admission(s) prior to baseline, currently smoking, FVC, CDS total score, and CIROPD%correct allows clinicians to predict the probability of an eCOPD-related hospitalization.

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Medication Adherence and Associated Factors among Chronic Heart Failure Clients on Follow Up Oromia Region, West Ethiopia

Cardiovascular & Hematological Agents in Medicinal Chemistry ◽

10.2174/1871525717666191019162254 ◽

2019 ◽

Vol 17 (2) ◽

pp. 104-114

Author(s):

Getahun Fetensa ◽

Birhanu Yadecha ◽

Tadesse Tolossa ◽

Tariku Tesfaye Bekuma

Keyword(s):

Heart Failure ◽

Medication Adherence ◽

Chronic Heart Failure ◽

Health Education ◽

Study Design ◽

Associated Factors ◽

Multivariable Analysis ◽

Self Care ◽

P Value

Background: Chronic heart failure is a complex clinical syndrome with typical symptoms that can occur at rest or on effort. It requires patients to manage their lifestyle with their disease and when to notify their healthcare provider. The study was aimed to identify medication adherence and associated factors among chronic heart failure clients on follow up Oromia region, West Ethiopia, 2017. Methods: Institutional based cross-sectional study design was employed, after selecting three hospitals by lottery method and allocating respondents to the three hospitals proportionally. A total of 424 patients were admitted to the medical ward and/or chronic follow up of Nekemte referral, Gimbi, and Shambu hospitals. The data was collected using a structured questionnaire. The data was entered into Epi-data version 3.1, cleared, explored, and then exported to SPSS windows version 24.0 for further analysis. Variable having a p-value less than 0.05 in the bivariate analysis was a candidate for multivariable analysis and the effect of confounding variables was observed. Variables having a p-value less than 0.05 in the multivariable analysis were assumed significant. Results: A total of 424 respondents were included in the final analysis giving a 95.3% response rate. The result indicated that more than half of the study participants have adhered to prescribed medication. Respondents with good medication adherence were more likely to adhere to good self-care behavior [AOR (95% CI of OR) = 3.5(2.044, 5.96)]. Respondents whose limited fluid intake was one or half-liter per day were more likely to adhere to the medication [AOR (95% CI of OR) = 2.5(1.43, 4.49)]. It was also found that those patients who avoided spices, sauces and others in food are more likely to adhere to the medication [AOR (95% CI of OR) = 2.2 (1.152, 4.039)]. Conclusion and Recommendation: Even if more than half of the study respondents have good medication and self-care adherence, still it needs great attention in health education over their visit. Health institutions are strongly recommended to give health education for clients and researchers to use advanced study design for measuring medication adherence and self-care behaviors.

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