Clinical and laboratory profile of hospitalized patients affected by pressure injury

Objective: To characterize the clinical and laboratory profile of hospitalized patients affected by pressure injury (PI). Method: Retrospective and descriptive study, which included data from electronic medical records of 95 patients affected by PI during hospitalization. Results: There was a predominance of females (52.6%), mean age 74.8 ± 14 years, mean hospital stay was 76.9 ± 88.8 days. Most were admitted to the intensive care unit, with an average of 17.86 ± 36.58 days. Regarding the clinical condition, 60% were using a mechanical ventilator when they developed PI, 37.9% needed hemodialysis, 30.4% were diagnosed with some degree of protein-energy malnutrition and 54.7% progressed to death. The most frequent comorbidities were hypertension (63.16%), diabetes (43.16%) and neuropathy (33.68%). As for the laboratory profile, hypoalbuminemia (97.3%), hyperglycemia (87.8%), anemia (84.4%) and hyperuremia (78.9%) were present in more than two thirds of the sample. Conclusion: This study allowed to know the profile of patients affected by PI during hospitalization, which can serve as a basis for developing scientifically based effective preventive actions.

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Low ALT values amongst hospitalized patients are associated with increased risk of hypoglycemia and overall mortality: a retrospective, big-data analysis of 51 831 patients

QJM ◽

10.1093/qjmed/hcaa219 ◽

2020 ◽

Cited By ~ 1

Author(s):

E Itelman ◽

A Segev ◽

L Ahmead ◽

E Leibowitz ◽

M Agbaria ◽

...

Keyword(s):

Electronic Medical Records ◽

Medical Records ◽

Cohort Analysis ◽

Surrogate Marker ◽

Tertiary Hospital ◽

Big Data Analysis ◽

Hospitalized Patients ◽

Retrospective Cohort Analysis ◽

Increased Risk ◽

Overall Mortality

Summary Background Sarcopenia and frailty influence clinical patients’ outcomes. Low alanine aminotransferase (ALT) serum activity is a surrogate marker for sarcopenia and frailty. In-hospital hypoglycemia is associated, also with worse clinical outcomes. Aim We evaluated the association between low ALT, risk of in-hospital hypoglycemia and subsequent mortality. Design This was a retrospective cohort analysis. Methods We included patients hospitalized in a tertiary hospital between 2007 and 2019. Patients’ data were retrieved from their electronic medical records. Results The cohort included 51 831 patients (average age 70.88). The rate of hypoglycemia was 10.8% (amongst diabetics 19.4% whereas in non-diabetics 8.3%). The rate of hypoglycemia was higher amongst patients with ALT < 10 IU/l in the whole cohort (14.3% vs. 10.4%, P < 0.001) as well as amongst diabetics (24.6% vs. 18.8%, P < 0.001). Both the overall and in-hospital mortality were higher in the low ALT group (57.7% vs. 39.1% P < 0.001 and 4.3% vs. 3.2%, P < 0.001). A propensity score matching, after which a regression model was performed, showed that patients with ALT levels < 10 IU/l had higher risk of overall mortality (HR = 1.21, CI 1.13–1.29, P < 0.001). Conclusions Low ALT values amongst hospitalized patients are associated with increased risk of in-hospital hypoglycemia and overall mortality.

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Utility of Electronic Medical Records to Assess the Relationship Between Parenteral Nutrition and Central Line–Associated Bloodstream Infections in Adult Hospitalized Patients

Journal of Parenteral and Enteral Nutrition ◽

10.1177/0148607114536580 ◽

2014 ◽

Vol 39 (8) ◽

pp. 929-934 ◽

Cited By ~ 6

Author(s):

Paul Ippolito ◽

Elaine L. Larson ◽

E. Yoko Furuya ◽

Jianfang Liu ◽

David S. Seres

Keyword(s):

Parenteral Nutrition ◽

Electronic Medical Records ◽

Medical Records ◽

Bloodstream Infections ◽

Central Line ◽

Hospitalized Patients ◽

The Relationship

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Prealbumin Serum Concentrations as a Useful Tool in the Assessment of Malnutrition in Hospitalized Patients

Clinical Chemistry ◽

10.1373/clinchem.2006.080366 ◽

2006 ◽

Vol 52 (12) ◽

pp. 2281-2285 ◽

Cited By ~ 109

Author(s):

Gianluigi Devoto ◽

Fabrizio Gallo ◽

Concetta Marchello ◽

Omar Racchi ◽

Roberta Garbarini ◽

...

Keyword(s):

Nutritional Assessment ◽

Protein Energy Malnutrition ◽

Reference Method ◽

Hospitalized Patients ◽

Severe Malnutrition ◽

Serum Concentrations ◽

Nutritional Index ◽

Protein Energy ◽

Sensitivity Specificity ◽

Worse Prognosis

Abstract Background: Protein-energy malnutrition (PEM) is a common condition among patients admitted to hospitals, and it is associated with a worse prognosis and increased mortality. Although several screening systems have been developed, PEM is still poorly recognized, and there is no consensus on which test is more reliable and feasible in clinical practice. Prealbumin (PAB) is a potential useful PEM marker because its serum concentrations are closely related to early changes in nutritional status. Methods: We studied PEM prevalence and PAB serum concentrations in 108 hospitalized patients. The Detailed Nutritional Assessment (DNA) was used as the reference method to determine PEM. PAB performance was compared with that of 2 other methods, the Subjective Global Assessment (SGA) and the Prognostic Inflammatory and Nutritional Index score (PINI). Results: According to the DNA reference method, 41% of patients were classified with mild malnutrition and 19% with severe malnutrition. PAB showed the best concordance with the standard DNA method (concordance index, 76.8%) and a good sensitivity/specificity profile (83.1%/76.7%) compared with SGA and PINI. Conclusions: We conclude that PAB could represent a feasible and reliable tool in the evaluation of malnutrition, especially in settings where it is difficult to obtain a more detailed and comprehensive nutritional assessment such as the DNA.

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Frequency and clinical characteristics of adverse transfusion reactions in hospitalized patients: A retrospective review of electronic medical records

Allergy Asthma & Respiratory Disease ◽

10.4168/aard.2021.9.4.225 ◽

2021 ◽

Vol 9 (4) ◽

pp. 225

Author(s):

Yong-Hyun Kim ◽

Jang-Ho Seo ◽

Kyung-Min Ahn ◽

Min-Suk Yang ◽

Sae-Hoon Kim ◽

...

Keyword(s):

Electronic Medical Records ◽

Retrospective Review ◽

Medical Records ◽

Clinical Characteristics ◽

Hospitalized Patients ◽

Transfusion Reactions

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The Effect of Antibiotics and Drugs on the Duration of COVID-19 in Hospitalized Patients

Journal of Pharmaceutical Research International ◽

10.9734/jpri/2021/v33i47a32988 ◽

2021 ◽

pp. 49-58

Author(s):

Amin Khattab ◽

Fahad Dakilallah Aljohani ◽

Roa Halawani ◽

Heba Mahmoud Eltahir ◽

Ahmed M. Shehata ◽

...

Keyword(s):

Medical Records ◽

Time Course ◽

Antibacterial Agents ◽

Antiviral Agents ◽

Hospitalized Patients ◽

Mechanical Ventilator ◽

Single Center ◽

Kingdom Of Saudi Arabia ◽

Ventilator Support ◽

Course Of Disease

This is a single center, retrospective, observational study carried out in Ohud hospital, the main referral hospital for SARS-CoV-2 infections in the region of Madinah, Kingdom of Saudi Arabia. The study was carried on hospitalized patients with moderate to severe symptoms, including critically ill patients in the intensive care unit mostly of them requiring oxygen or mechanical ventilator support. Medical records from 432 cases were investigated showing that the majority of infected population were adults with an average age of 48 years, where 68.3% were males and the mortality rate was 5.6%. Duration of the disease was determined as the period between the first positive and the first negative PCR results. Patients who received antibiotics or Metoclopramide showed shorter duration of the disease time course while those who received Hydroxychloroquine, Omeprazole or Calcium exhibited longer durations before obtaining a negative PCR result. Regression analysis furtherly confirmed that antibiotics administration was associated with shorter course of disease while hydroxychloroquine or omeprazole were correlated with longer duration of the disease. Antiviral drugs, however, showed no correlations to the COVID-19 duration of stay in hospital. Finally, combining antibiotics and antiviral agents did not result in a better outcome, suggesting that the use of antibacterial agents helps in the recovery of SARS-CoV-2 patients.

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Gimme 3 Steps (With a Nod to an American Rock Song from the 1970s)

The Heart Surgery Forum ◽

10.1532/hsf.1565 ◽

2016 ◽

Vol 19 (2) ◽

pp. 080

Author(s):

Curt G Tribble

Keyword(s):

Electronic Medical Records ◽

National Survey ◽

Medical Records ◽

Hospitalized Patients ◽

Physician Burnout ◽

Long-term risk of hepatocellular carcinoma mortality in 23220 hospitalized patients treated with micafungin or other parenteral antifungals

Journal of Antimicrobial Chemotherapy ◽

10.1093/jac/dkz396 ◽

2019 ◽

Vol 75 (1) ◽

pp. 221-228 ◽

Cited By ~ 2

Author(s):

Sebastian Schneeweiss ◽

Peggy L Carver ◽

Kausik Datta ◽

Alicia Galar ◽

Melissa D Johnson ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Electronic Medical Records ◽

Medical Records ◽

Patient Characteristics ◽

Hospitalized Patients ◽

Liver Tumours ◽

Event Rates ◽

Multicentre Cohort

Abstract Background Liver tumours observed in rats exposed to micafungin led to a black box warning upon approval in Europe in 2008. Micafungin’s risk for liver carcinogenicity in humans has not been investigated. We sought to describe the risk of fatal hepatocellular carcinoma (HCC) among persons who received micafungin and other parenteral antifungals (PAFs) with up to 12 years of follow-up. Methods We assembled a US multicentre cohort of hospitalized patients who received micafungin or other PAFs between 2005 and 2012. We used propensity score (PS) matching on patient characteristics from electronic medical records to compare rates of HCC mortality identified through the National Death Index though to the end of December 2016. We computed HRs and 95% CIs. Results A total of 40110 patients who received a PAF were identified; 6903 micafungin recipients (87% of those identified) were successfully matched to 16317 comparator PAF users. Ten incident HCC deaths, one in the micafungin-exposed group and nine among comparator PAF users, occurred in 71285 person-years of follow-up. The HCC mortality rate was 0.05 per 1000 person-years in micafungin patients and 0.17 per 1000 person-years in comparator PAF patients. The PS-matched HR for micafungin versus comparator PAF was 0.29 (95% CI 0.04–2.24). Conclusions Both micafungin and comparator PAFs were associated with HCC mortality rates of <0.2 per 1000 person-years. Given the very low event rates, any potential risk for HCC should not play a role in clinical decisions regarding treatment with micafungin or other PAFs investigated in this study.

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Predicting Antibiotic Resistance in Hospitalized Patients by Applying Machine Learning to Electronic Medical Records

Clinical Infectious Diseases ◽

10.1093/cid/ciaa1576 ◽

2020 ◽

Cited By ~ 2

Author(s):

Ohad Lewin-Epstein ◽

Shoham Baruch ◽

Lilach Hadany ◽

Gideon Y Stein ◽

Uri Obolski

Keyword(s):

Machine Learning ◽

Antibiotic Resistance ◽

Electronic Medical Records ◽

Medical Records ◽

Bacterial Infections ◽

Bacterial Species ◽

Empiric Therapy ◽

Hospitalized Patients ◽

Empiric Antibiotic Therapy ◽

Computerized Decision Support

Abstract Background Computerized decision support systems are becoming increasingly prevalent with advances in data collection and machine learning (ML) algorithms. However, they are scarcely used for empiric antibiotic therapy. Here, we predict the antibiotic resistance profiles of bacterial infections of hospitalized patients using ML algorithms applied to patients’ electronic medical records (EMRs). Methods The data included antibiotic resistance results of bacterial cultures from hospitalized patients, alongside their EMRs. Five antibiotics were examined: ceftazidime (n = 2942), gentamicin (n = 4360), imipenem (n = 2235), ofloxacin (n = 3117), and sulfamethoxazole-trimethoprim (n = 3544). We applied lasso logistic regression, neural networks, gradient boosted trees, and an ensemble that combined all 3 algorithms, to predict antibiotic resistance. Variable influence was gauged by permutation tests and Shapely Additive Explanations analysis. Results The ensemble outperformed the separate models and produced accurate predictions on test set data. When no knowledge regarding the infecting bacterial species was assumed, the ensemble yielded area under the receiver-operating characteristic (auROC) scores of 0.73–0.79 for different antibiotics. Including information regarding the bacterial species improved the auROCs to 0.8–0.88. Variables’ effects on predictions were assessed and found to be consistent with previously identified risk factors for antibiotic resistance. Conclusions We demonstrate the potential of ML to predict antibiotic resistance of bacterial infections of hospitalized patients. Moreover, we show that rapidly gained information regarding the infecting bacterial species can improve predictions substantially. Clinicians should consider the implementation of such systems to aid correct empiric therapy and to potentially reduce antibiotic misuse.

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Predicting antibiotic resistance in hospitalized patients by applying machine learning to electronic medical records

10.1101/2020.06.03.20120535 ◽

2020 ◽

Author(s):

Ohad Lewin-Epstein ◽

Shoham Baruch ◽

Lilach Hadany ◽

Gideon Y Stein ◽

Uri Obolski

Keyword(s):

Machine Learning ◽

Antibiotic Resistance ◽

Electronic Medical Records ◽

Medical Records ◽

Bacterial Infections ◽

Bacterial Species ◽

Hospitalized Patients ◽

Machine Learning Algorithms ◽

Ensemble Model ◽

Machine Learning Models

AbstractBackgroundComputerized decision support systems are becoming increasingly prevalent with advances in data collection and machine learning algorithms. However, they are scarcely used for empiric antibiotic therapy. Here we accurately predict the antibiotic resistance profiles of bacterial infections of hospitalized patients using machine learning algorithms applied to patients’ electronic medical records.MethodsThe data included antibiotic resistance results of bacterial cultures from hospitalized patients, alongside their electronic medical records. Five antibiotics were examined: Ceftazidime (n=2942), Gentamicin (n=4360), Imipenem (n=2235), Ofloxacin (n=3117) and Sulfamethoxazole-Trimethoprim (n=3544). We applied lasso logistic regression, neural networks, gradient boosted trees, and an ensemble combining all three algorithms, to predict antibiotic resistance. Variable influence was gauged by permutation tests and Shapely Additive Explanations analysis.ResultsThe ensemble model outperformed the separate models and produced accurate predictions on a test set data. When no knowledge regarding the infecting bacterial species was assumed, the ensemble model yielded area under the receiver-operating-characteristic (auROC) scores of 0.73-0.79, for different antibiotics. Including information regarding the bacterial species improved the auROCs to 0.8-0.88. The effects of different variables on the predictions were assessed and found consistent with previously identified risk factors for antibiotic resistance.ConclusionsOur study demonstrates the potential of machine learning models to accurately predict antibiotic resistance of bacterial infections of hospitalized patients. Moreover, we show that rapid information regarding the infecting bacterial species can improve predictions substantially. The implementation of such systems should be seriously considered by clinicians to aid correct empiric therapy and to potentially reduce antibiotic misuse.40-word summaryMachine learning models were applied to large and diverse datasets of medical records of hospitalized patients, to predict antibiotic resistance profiles of bacterial infections. The models achieved high accuracy predictions and interpretable results regarding the drivers of antibiotic resistance.

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Terms of specialized nursing language for people with pressure injury

Revista Brasileira de Enfermagem ◽

10.1590/0034-7167-2018-0492 ◽

2019 ◽

Vol 72 (4) ◽

pp. 1028-1035

Author(s):

Fernando Hiago da Silva Duarte ◽

Wenysson Noleto dos Santos ◽

Francisca Simone da Silva ◽

Dhyanine Morais de Lima ◽

Sâmara Fontes Fernandes ◽

...

Keyword(s):

Medical Records ◽

Nursing Practice ◽

Hospitalized Patients ◽

International Classification ◽

Methodological Study ◽

Professional Nursing ◽

Pressure Injury ◽

Nursing Language

ABSTRACT Objective: to validate the terms of the specialized nursing language used in care for hospitalized patients with Pressure Injury, identified in nurses’ records, mapping them with the terms of the International Classification for Nursing Practice (ICNP® 2017). Methods: methodological study, carried out at a School Hospital in 2018. It was performed: extraction of terms of medical records; normalization; cross-mapping between extracted terms and those in ICNP®; distribution in the seven axes; theoretical definition and validation of terms. Results: 27,756 terms were extracted. The normalization resulted in 370 relevant terms, being: 225 listed and 145 not listed in the ICNP®, being 60 similar, 13 more comprehensive, 38 more restricted and 34 without agreement, all of which are validated. Conclusion: this study identified and validated terms used by nurses to assist people with Pressure Injury. These may contribute to the unification of professional nursing language in care for these clients.

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