Risk factors and clinical outcome of hypomagnesemic patients in pediatric intensive care

Background: Hypomagnesemia is a common finding in current medical practice, especially in critically ill patients. Magnesium ion plays a vital role in various metabolic processes in body and its deficiency leading to serious clinical consequences. Since hypomagnesemia is most often asymptomatic, it goes unsuspected and therefore undiagnosed. Hence, early detection of hypomagnesemia has prognostic and therapeutic implications. It is imperative to understand the various risk factors and their clinical outcome that is associated with hypomagnesemia.Methods: This is an observational study done in a tertiary centre in Bangalore, India where-in 100 children who met the inclusion criteria, admitted to the PICU were recruited and prospectively studied. Serum Magnesium along with various clinical and biochemical parameters were correlated to enumerate the various risk factors associated with hypomagnesemia.Results: In this study authors found the incidence of hypomagnesemia to be around 53%. Authors found higher incidence in age group of 1-5 yrs (40%) and least were in the age groups of <1 year and more than 10 years (19%) and there was no gender preponderance. Authors also evaluated the various risk factors associated with hypomagnesemia. There was significant association of hypocalcemia (60%) and hypokalemia (45.2%) with hypomagnesemia. Infections (33.9%) and neurological disorders (26.41%) seemed to collectively comprise around 60% of the hypomagnesemic group. All patients admitted secondary to sepsis and Traumatic Brain Injury (TBI) had hypomagnesemia proving to be a significant risk factor. Authors also found increased mortality among hypomagnesemic group. However, found no association between low serum magnesium and PICU stay.Conclusions: There is high prevalence of hypomagnesemia in critically ill patients and is associated with a higher mortality. It is also commonly associated with infections, CNS disorders, respiratory diseases and metabolic derangements like hypokalaemia and hypocalcaemia. There is no association of Hypomagnesemia with duration of PICU stay.

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Risk factors associated with Acinetobacter baumannii septicemia and its mortality rates in critically ill patients

Critical Care ◽

10.1186/cc12015 ◽

2013 ◽

Vol 17 (S2) ◽

Author(s):

K Kontopoulou ◽

K Tsepanis ◽

I Sgouropoulos ◽

A Triantafyllidou ◽

D Socratous ◽

...

Keyword(s):

Risk Factors ◽

Acinetobacter Baumannii ◽

Critically Ill ◽

Critically Ill Patients ◽

Mortality Rates ◽

Factors Associated

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Risk factors associated with the onset of daptomycin non-susceptibility in Staphylococcus aureus infections in critically ill patients

Intensive Care Medicine ◽

10.1007/s00134-014-3571-6 ◽

2014 ◽

Vol 41 (2) ◽

pp. 366-368 ◽

Cited By ~ 11

Author(s):

Matteo Bassetti ◽

Giovanni Villa ◽

Filippo Ansaldi ◽

Daniela De Florentiis ◽

Carlo Tascini ◽

...

Keyword(s):

Risk Factors ◽

Staphylococcus Aureus ◽

Critically Ill ◽

Critically Ill Patients ◽

Factors Associated

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Intraoperative Risk Factors Associated With Postoperative Pressure Ulcers in Critically Ill Patients

Critical Care Medicine ◽

10.1097/ccm.0b013e318298a849 ◽

2014 ◽

Vol 42 (1) ◽

pp. 40-47 ◽

Cited By ~ 38

Author(s):

Denise D. O’Brien ◽

Amy M. Shanks ◽

AkkeNeel Talsma ◽

Phyllis S. Brenner ◽

Satya Krishna Ramachandran

Keyword(s):

Risk Factors ◽

Critically Ill ◽

Critically Ill Patients ◽

Pressure Ulcers ◽

Factors Associated

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Characteristics and Risk Factors Associated With Mortality in Critically Ill Patients With COVID-19

Cureus ◽

10.7759/cureus.14442 ◽

2021 ◽

Author(s):

Yannick Vogels ◽

Sjaak Pouwels ◽

Jos van Oers ◽

Dharmanand Ramnarain

Keyword(s):

Risk Factors ◽

Critically Ill ◽

Critically Ill Patients ◽

Factors Associated

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Augmented renal clearance is a common finding with worse clinical outcome in critically ill patients receiving antimicrobial therapy

Journal of Critical Care ◽

10.1016/j.jcrc.2013.03.003 ◽

2013 ◽

Vol 28 (5) ◽

pp. 695-700 ◽

Cited By ~ 101

Author(s):

Barbara O.M. Claus ◽

Eric A. Hoste ◽

Kirsten Colpaert ◽

Hugo Robays ◽

Johan Decruyenaere ◽

...

Keyword(s):

Clinical Outcome ◽

Renal Clearance ◽

Critically Ill ◽

Antimicrobial Therapy ◽

Critically Ill Patients ◽

Common Finding ◽

Augmented Renal Clearance

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Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain

Critical Care ◽

10.1186/s13054-021-03487-8 ◽

2021 ◽

Vol 25 (1) ◽

Author(s):

Alejandro Rodríguez ◽

◽

Manuel Ruiz-Botella ◽

Ignacio Martín-Loeches ◽

María Jimenez Herrera ◽

...

Keyword(s):

Risk Factors ◽

Critically Ill ◽

Mortality Risk ◽

Critically Ill Patients ◽

Clustering Analysis ◽

Unsupervised Clustering ◽

Older Age ◽

Icu Mortality ◽

Factors Associated ◽

Clinical Phenotypes

Abstract Background The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. Methods Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient’s factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. Results The database included a total of 2022 patients (mean age 64 [IQR 5–71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10–17]) and SOFA score (5 [IQR 3–7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age (< 65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623, 30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C (severe) phenotype was the most common (857; 42.5%) and was characterized by the interplay of older age (> 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications. Conclusion The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice.

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Development a Nomogram to Predict Prognosis in Severe and Critically Ill Patients with COVID-19

10.21203/rs.3.rs-34264/v1 ◽

2020 ◽

Author(s):

Weiping Tan ◽

Ying Zhu ◽

Hui Yi ◽

Yingyu Lin ◽

Yumei Liu ◽

...

Keyword(s):

Risk Factors ◽

Disease Severity ◽

Critically Ill ◽

Critically Ill Patients ◽

Poor Prognosis ◽

Total Bilirubin ◽

Lung Involvement ◽

Factors Associated ◽

Independent Risk Factors ◽

D Dimer

Abstract Background: The number of deaths caused by COVID-19 are on the rising worldwide. This study focused on severe and critically ill COVID-19, aim to explore independent risk factors associated with disease severity and to build a nomogram to predict patients’ prognosis.Methods: Patients with laboratory-confirmed COVID-19 admitted to the Union Hospital, Tongji Medical College and Hankou Hospital of Wuhan, China, from February 8th to April 6th, 2020. LASSO Regression and Multivariate Analysis were applied to screen independent factors. COX Nomogram was built to predict the 7-day, 14-day and 1-month survival probability.Results: A total of 115 severe [73 (63.5%)] and critically ill [42 (36.5%)] patients were included in this study, containing 93 (80.9%) survivors and 22 (19.1%) non-survivors. For disease severity, D-dimer [OR 6.33 (95%CI, 1.27-45.57], eosinophil percentage [OR 8.02 (95%CI, 1.82-45.04)], total bilirubin [OR 12.38 (95%CI, 1.24-223.65)] and lung involvement score [OR 1.22 (95%CI, 1.08-1.40)] were the independent factors associated with critical illness. Troponin [HR 9.02 (95%CI, 3.02, 26.97)] and total bilirubin [HR 3.16 (95%CI, 1.13, 8.85)] were the independent predictors for patients’ prognosis. Troponin≥26.2 ng/L and total bilirubin＞20 μmol/L were associated with poor prognosis. The nomogram based on the independent risk factors had a C-index of 0.92 (95%CI, 0.87, 0.98) for predicting survival probability. The survival nomogram validated in the critically ill patients had a C-index of 0.83 (95%CI: 0.75, 0.94).Conclusions: In conclusion, in severe and critically ill patients with COVID-19, D-dimer, eosinophil percentage, total bilirubin and lung involvement score were the independent risk factors associated with disease severity. The proposed survival nomogram accurately predicted prognosis. The survival analysis may suggest that early incidence of multiple organ dysfunction may be an important predictor of poor prognosis.

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Air leak syndromes (Pneumomediastinum, pneumothorax, and subcutaneous emphysema) in Critically ill COVID – 19 patients – Prevalence, risk factors, and outcome.

10.21203/rs.3.rs-1154310/v1 ◽

2021 ◽

Author(s):

Parth Sharma ◽

Rakesh Mohanty ◽

Preethi Kuryan ◽

Sheetal Babu ◽

Manisha Mane ◽

...

Keyword(s):

Risk Factors ◽

Hospital Stay ◽

Critically Ill ◽

Significant Risk ◽

Positive Pressure ◽

High Peep ◽

Air Leak ◽

Factors Associated ◽

Duration Of Hospital Stay ◽

High Incidence

Abstract BACKGROUND: A high incidence of air leak syndromes (ALS) has been reported in critically ill COVID-19 patients. This not only prolongs the hospital stay of patients but also affects the disease outcome.OBJECTIVE: Our objective is to evaluate the incidence, clinical outcome, and risk factors associated with ALS in critically ill COVID-19 patients receiving invasive or non-invasive positive pressure ventilationRESULT: Out of 79 patients, 16(20.2%) patients had ALS. The mean age of the ALS group was 48.6±13.1 years as compared to 52.8±13.1 (p = 0.260) years in the non-ALS group. The ALS group had a lower median BMI (25.9 kg/m2 vs 27.6 kg/m2 , p = 0.096), a higher D-dimer value at presentation (1179.5 vs 762.0, p = 0.024) , lower saturation (74% vs 88%, p = 0.006) and lower PF (134 vs 189, p = 0.028) ratio at presentation as compared to the ALS group. Patients who developed ALS were found to have received a higher median PEEP (10 cm vs 8 cm of water, p = 0.005). Pressure support, highest driving pressure, and peak airway pressure were not significantly different in the two groups. ALS group was seen to have a significantly longer duration of hospital stay (17.5 days vs 9 days, p = 0.003). Multiple Logistic Regressions analysis indicated patients who received Inj. Dexamethasone was less likely to develop ALS (OR: 12.6 (95% CI 1.6-95.4), p=0.015). CONCLUSION: A high incidence of ALS is present in critically ill COVID 19 patients. High inflammatory parameters, severe hypoxia at presentation, and use of high PEEP are significant risk factors associated with the development of ALS. The risk of developing ALS was observed to be lower in patients who received Inj. Dexamethasone. ALS is associated with a longer duration of hospital stay.

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Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2,022 critically ill patients with COVID-19 in Spain

10.21203/rs.3.rs-125422/v2 ◽

2021 ◽

Author(s):

Alejandro Rodríguez ◽

Manuel Ruiz Botella ◽

Ignacio Matín-Loeches ◽

María Jiménez Herrera ◽

Jordi Solé-Violan ◽

...

Keyword(s):

Risk Factors ◽

Intensive Care ◽

Critically Ill ◽

Mortality Risk ◽

Critically Ill Patients ◽

Clustering Analysis ◽

Older Age ◽

Icu Mortality ◽

Factors Associated ◽

Clinical Phenotypes

Abstract Background: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. Methods: Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 Intensive Care Units(ICU) in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient’s factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. Results: The database included a total of 2,022 patients (mean age 64[IQR5-71] years, 1423(70.4%) male, median APACHE II score (13[IQR10-17]) and SOFA score (5[IQR3-7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A(mild) phenotype (537;26.7%) included older age (<65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623,30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C(severe) phenotype was the most common (857;42.5%) and was characterized by the interplay of older age (>65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications.Conclusion: The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice.

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Risk Factors for Development of Acute Kidney Injury in Critically Ill Patients: A Systematic Review and Meta-Analysis of Observational Studies

Critical Care Research and Practice ◽

10.1155/2012/691013 ◽

2012 ◽

Vol 2012 ◽

pp. 1-15 ◽

Cited By ~ 26

Author(s):

Rodrigo Cartin-Ceba ◽

Markos Kashiouris ◽

Maria Plataki ◽

Daryl J. Kor ◽

Ognjen Gajic ◽

...

Keyword(s):

Risk Factors ◽

Acute Kidney Injury ◽

High Risk ◽

Critically Ill ◽

Critically Ill Patients ◽

Observational Studies ◽

Kidney Injury ◽

Factors Associated ◽

Meta Analyses ◽

The Impact

Background. Acute kidney injury (AKI) is a frequent complication of critically ill patients. The impact of different risk factors associated with this entity in the ICU setting is unknown.Objectives. The purpose of this research was to assess the risk factors associated with the development of AKI in critically ill patients by meta-analyses of observational studies.Data Extraction. Two reviewers independently and in duplicate used a standardized form to collect data from published reports. Authors were contacted for missing data. The Newcastle-Ottawa scale assessed study quality.Data Synthesis. Data from 31 diverse studies that enrolled 504,535 critically ill individuals from a wide variety of ICUs were included. Separate random-effects meta-analyses demonstrated a significantly increased risk of AKI with older age, diabetes, hypertension, higher baseline creatinine, heart failure, sepsis/systemic inflammatory response syndrome, use of nephrotoxic drugs, higher severity of disease scores, use of vasopressors/inotropes, high risk surgery, emergency surgery, use of intra-aortic balloon pump, and longer time in cardiopulmonary bypass pump.Conclusion. The best available evidence suggests an association of AKI with 13 different risk factors in subjects admitted to the ICU. Predictive models for identification of high risk individuals for developing AKI in all types of ICU are required.

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