Dataset Information

Development and validation of a clinical instrument to predict risk of an adverse drug reactions in hospitalized patients.

ABSTRACT:

Objective

Development and internal validation of a clinical tool for assessment of the risk of adverse drug reactions (ADR) in hospitalized patients.

Methodology

Nested case-control study in an open cohort of all patients admitted to a general hospital. Cases of ADR were matched to two controls. Eighty four patient variables collected at the time of the ADR were analyzed by conditional logistic regression. Multivariate logistic regression with clustering of cases in a random sample of 2/3 of the cases and respective controls, with baseline odds-ratio corrected with an estimate of ADR incidence, was used to obtain regression coefficients for each risk factor and to develop a risk score. The clinical tool was validated in the remaining 1/3 observations. The study was approved by the institution's research ethics committee.

Results

In the 8060 hospitalized patients, ADR occurred in 343 (5.31%), who were matched to 686 controls. Fourteen variables were identified as independent risk factors of ADR: female, past history of ADR, heart rate ?72 bpm, systolic blood pressure?148 mmHg, diastolic blood pressure <79 mmHg, diabetes mellitus, serum urea ? 67 mg/dL, serum sodium ?141 mmol/L, serum potassium ?4.9 mmol/L, main diagnosis of neoplasia, prescription of ?3 ATC class B drugs, prescription of ATC class R drugs, prescription of intravenous drugs and ? 6 oral drugs. In the validation sample, the ADR risk tool based on those variables showed sensitivity 61%, specificity 73% and area under the ROC curve 0.73.

Conclusion

We report a clinical tool for ADR risk stratification in patients hospitalized in general wards based on 14 variables.

SUBMITTER: Lima SIVC

PROVIDER: S-EPMC7732084 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and validation of a clinical instrument to predict risk of an adverse drug reactions in hospitalized patients.

Lima Sara Iasmin Vieira Cunha SIVC Martins Rand Randall RR Saldanha Valdjane V Silbiger Vivian Nogueira VN Dos Santos Isabelle Cristina Clemente ICC Araújo Ivonete Batista de IB Oliveira Antonio Gouveia AG

PloS one 20201211 12

<h4>Objective</h4>Development and internal validation of a clinical tool for assessment of the risk of adverse drug reactions (ADR) in hospitalized patients.<h4>Methodology</h4>Nested case-control study in an open cohort of all patients admitted to a general hospital. Cases of ADR were matched to two controls. Eighty four patient variables collected at the time of the ADR were analyzed by conditional logistic regression. Multivariate logistic regression with clustering of cases in a random sampl ...[more]

PMID: 33306728

Similar Datasets

Project description:BackgroundWomen experience adverse drug reactions, ADRs, nearly twice as often as men, yet the role of sex as a biological factor in the generation of ADRs is poorly understood. Most drugs currently in use were approved based on clinical trials conducted on men, so women may be overmedicated. We determined whether sex differences in drug pharmacokinetics, PKs, predict sex differences in ADRs.MethodsSearches of the ISI Web of Science and PubMed databases were conducted with combinations of the terms: drugs, sex or gender, pharmacokinetics, pharmacodynamics, drug safety, drug dose, and adverse drug reaction, which yielded over 5000 articles with considerable overlap. We obtained information from each relevant article on significant sex differences in PK measures, predominantly area under the curve, peak/maximum concentrations, and clearance/elimination rates. ADRs were identified from every relevant article and recorded categorically as female-biased, male-biased, or not sex-biased.ResultsFor most of the FDA-approved drugs examined, elevated blood concentrations and longer elimination times were manifested by women, and these PKs were strongly linked to sex differences in ADRs. Of the 86 drugs evaluated, 76 had higher PK values in women; for 59 drugs with clinically identifiable ADRs, sex-biased PKs predicted the direction of sex-biased ADRs in 88% of cases. Ninety-six percent of drugs with female-biased PK values were associated with a higher incidence of ADRs in women than men, but only 29% of male-biased PKs predicted male-biased ADRs. Accessible PK information is available for only a small fraction of all drugs CONCLUSIONS: Sex differences in pharmacokinetics strongly predict sex-specific ADRs for women but not men. This sex difference was not explained by sex differences in body weight. The absence of sex-stratified PK information in public records for hundreds of drugs raises the concern that sex differences in PK values are widespread and of clinical significance. The common practice of prescribing equal drug doses to women and men neglects sex differences in pharmacokinetics and dimorphisms in body weight, risks overmedication of women, and contributes to female-biased adverse drug reactions. We recommend evidence-based dose reductions for women to counteract this sex bias.

Project description:ImportanceEarly identification of patients with novel coronavirus disease 2019 (COVID-19) who may develop critical illness is of great importance and may aid in delivering proper treatment and optimizing use of resources.ObjectiveTo develop and validate a clinical score at hospital admission for predicting which patients with COVID-19 will develop critical illness based on a nationwide cohort in China.Design, setting, and participantsCollaborating with the National Health Commission of China, we established a retrospective cohort of patients with COVID-19 from 575 hospitals in 31 provincial administrative regions as of January 31, 2020. Epidemiological, clinical, laboratory, and imaging variables ascertained at hospital admission were screened using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-GRAM). The score provides an estimate of the risk that a hospitalized patient with COVID-19 will develop critical illness. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). Data from 4 additional cohorts in China hospitalized with COVID-19 were used to validate the score. Data were analyzed between February 20, 2020 and March 17, 2020.Main outcomes and measuresAmong patients with COVID-19 admitted to the hospital, critical illness was defined as the composite measure of admission to the intensive care unit, invasive ventilation, or death.ResultsThe development cohort included 1590 patients. the mean (SD) age of patients in the cohort was 48.9 (15.7) years; 904 (57.3%) were men. The validation cohort included 710 patients with a mean (SD) age of 48.2 (15.2) years, and 382 (53.8%) were men and 172 (24.2%). From 72 potential predictors, 10 variables were independent predictive factors and were included in the risk score: chest radiographic abnormality (OR, 3.39; 95% CI, 2.14-5.38), age (OR, 1.03; 95% CI, 1.01-1.05), hemoptysis (OR, 4.53; 95% CI, 1.36-15.15), dyspnea (OR, 1.88; 95% CI, 1.18-3.01), unconsciousness (OR, 4.71; 95% CI, 1.39-15.98), number of comorbidities (OR, 1.60; 95% CI, 1.27-2.00), cancer history (OR, 4.07; 95% CI, 1.23-13.43), neutrophil-to-lymphocyte ratio (OR, 1.06; 95% CI, 1.02-1.10), lactate dehydrogenase (OR, 1.002; 95% CI, 1.001-1.004) and direct bilirubin (OR, 1.15; 95% CI, 1.06-1.24). The mean AUC in the development cohort was 0.88 (95% CI, 0.85-0.91) and the AUC in the validation cohort was 0.88 (95% CI, 0.84-0.93). The score has been translated into an online risk calculator that is freely available to the public (http://118.126.104.170/).Conclusions and relevanceIn this study, a risk score based on characteristics of COVID-19 patients at the time of admission to the hospital was developed that may help predict a patient's risk of developing critical illness.

Project description:BackgroundAdverse drug reactions (ADRs) are an important cause of harm in children. Current data are incomplete due to methodological differences between studies: only half of all studies provide drug data, incidence rates vary (0.6% to 16.8%) and very few studies provide data on causality, severity and risk factors of pediatric ADRs. We aimed to determine the incidence of ADRs in hospitalized children, to characterize these ADRs in terms of type, drug etiology, causality and severity and to identify risk factors.MethodsWe undertook a year-long, prospective observational cohort study of admissions to a single UK pediatric medical and surgical secondary and tertiary referral center (Alder Hey, Liverpool, UK). Children between 0 and 16 years 11 months old and admitted for more than 48 hours were included. Observed outcomes were occurrence of ADR and time to first ADR for the risk factor analysis.ResultsA total of 5,118 children (6,601 admissions) were included, 17.7% of whom experienced at least one ADR. Opiate analgesics and drugs used in general anesthesia (GA) accounted for more than 50% of all drugs implicated in ADRs. Of these ADRs, 0.9% caused permanent harm or required admission to a higher level of care. Children who underwent GA were at more than six times the risk of developing an ADR than children without a GA (hazard ratio (HR) 6.40; 95% confidence interval (CI) 5.30 to 7.70). Other factors increasing the risk of an ADR were increasing age (HR 1.06 for each year; 95% CI 1.04 to 1.07), increasing number of drugs (HR 1.25 for each additional drug; 95% CI 1.22 to 1.28) and oncological treatment (HR 1.90; 95% CI 1.40 to 2.60).ConclusionsADRs are common in hospitalized children and children who had undergone a GA had more than six times the risk of developing an ADR. GA agents and opiate analgesics are a significant cause of ADRs and have been underrepresented in previous studies. This is a concern in view of the increasing number of pediatric short-stay surgeries.

Dataset Information

Development and validation of a clinical instrument to predict risk of an adverse drug reactions in hospitalized patients.

Objective

Methodology

Results

Conclusion

Publications

Development and validation of a clinical instrument to predict risk of an adverse drug reactions in hospitalized patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets