Effects of peri-operative nonsteroidal anti-inflammatory drugs on post-operative kidney function for adults with normal kidney function

Background: Nonsteroidal anti-inflammatory drugs (NSAIDs) provide effective analgesia during the post-operative period but can cause acute kidney injury (AKI) when used peri-operatively (at or around the time of surgery). This is an update of a Cochrane review published in 2007.Objectives: This review looked at the effect of NSAIDs used in the peri-operative period on post-operative kidney function in patients with normal kidney function.Search methods: We searched Cochrane Kidney and Transplant’s Specialised Register to 4 January 2018 through contact with the Information Specialist using search terms relevant to this review. Studies in the Specialised Register are identified through searches of CENTRAL, MEDLINE, and EMBASE, conference proceedings, the International Clinical Trials Register (ICTRP) Search Portal and ClinicalTrials.gov.Selection criteria: All randomised controlled trials (RCTs) and quasi-RCTs (RCTs in which allocation to treatment was obtained by alternation, use of alternate medical records, date of birth or other predictable methods) looking at the use of NSAIDs versus placebo for the treatment of post-operative pain in patients with normal kidney function were included.Data collection and analysis: Data extraction was carried out independently by two authors as was assessment of risk of bias. Disagreements were resolved by a third author. Dichotomous outcomes are reported as relative risk (RR) and continuous outcomes as mean difference (MD) together with their 95% confidence intervals (CI). Meta-analyses were used to assess the outcomes of AKI, change in serum creatinine (SCr), urine output, renal replacement therapy (RRT), death (all causes) and length of hospital stay.Main results: We identified 26 studies (8835 participants). Risk of bias was high in 17, unclear in 6and low in three studies. There was high risk of attrition bias in six studies.Only two studies measured AKI. The use of NSAIDs had uncertain effects on the incidence of AKI compared to placebo (7066 participants: RR 1.79, 95% CI 0.40 to 7.96; I2 = 59%; very low certainty evidence). One study was stopped early by the data monitoring committee due to increased rates of AKI in the NSAID group. Moreover, both of these studies were examining NSAIDs for indications other than analgesia and therefore utilised relatively low doses. Compared to placebo, NSAIDs may slightly increase serum SCr (15 studies, 794 participants: MD 3.23 µmol/L, 95% CI -0.80 to 7.26; I2 = 63%; low certainty evidence). Studies displayed moderate to high heterogeneity and had multiple exclusion criteria including age and so were not representative of patients undergoing surgery. Three of these studies excluded patients if their creatinine rose postoperatively. NSAIDs may make little or no difference to post-operative urine output compared to placebo (6 studies, 149 participants: SMD - 0.02, 95% CI -0.31 to 0.27). No reliable conclusions could be drawn from these studies due to the differing units of measurements and measurement time points. It is uncertain whether NSAIDs leads to the need for RRT because the certainty of this evidence is very low (2 studies, 7056 participants: RR 1.57, 95% CI 0.49 to 5.07; I2 = 26%); there were few events and the results were inconsistent. It is uncertain whether NSAIDs lead to more deaths (2 studies, 312 participants: RR 1.44, 95% CI 0.19 to 11.12; I2 = 38%) or increased the length of hospital stay (3 studies, 410 participants: MD 0.12 days, 95% CI -0.48 to 0.72; I2 = 24%).Authors’ conclusions: Overall NSAIDs had uncertain effects on the risk of post-operative AKI, may slightly increase post-operative SCr, and it is uncertain whether NSAIDs lead to the need for RRT, death or increases the length of hospital stay. The available data therefore does not confirm the safety of NSAIDs in patients undergoing surgery. Further larger studies using the Kidney Disease Improving Global Outcomes definition for AKI including patients with co-morbidities are required to confirm these findings

Effects of peri-operative nonsteroidal anti-inflammatory drugs on postoperative kidney function for adults with normal kidney function

This is the protocol for a review and there is no abstract. The objectives are as follows: This review aims to look at the effect of NSAIDs used in the peri-operative period on post-operative kidney function in patients with normal kidney function.</p

Risk of postoperative acute kidney injury in patients undergoing orthopaedic surgery—development and validation of a risk score and effect of acute kidney injury on survival:observational cohort study

Funding: This study was funded by Tenovus Tayside, Chief Scientist Office, Scotland and a travelling fellowship from the Royal College of Physicians and Surgeons of Glasgow. The funders had no role in the study design; collection, analysis, and interpretation of the data; writing of the report; or the decision to submit the article for publication. The researchers are independent of the funders.Non peer reviewedPublisher PD

Identifying which septic patients have increased mortality risk using severity scores:a cohort study

Background: Early aggressive therapy can reduce the mortality associated with severe sepsis but this relies on prompt recognition, which is hindered by variation among published severity criteria. Our aim was to test the performance of different severity scores in predicting mortality among a cohort of hospital inpatients with sepsis. Methods: We anonymously linked routine outcome data to a cohort of prospectively identified adult hospital inpatients with sepsis, and used logistic regression to identify associations between mortality and demographic variables, clinical factors including blood culture results, and six sets of severity criteria. We calculated performance characteristics, including area under receiver operating characteristic curves (AUROC), of each set of severity criteria in predicting mortality. Results: Overall mortality was 19.4% (124/640) at 30 days after sepsis onset. In adjusted analysis, older age (odds ratio 5.79 (95% CI 2.87-11.70) for &ge;80y versus &lt;60y), having been admitted as an emergency (OR 3.91 (1.31-11.70) versus electively), and longer inpatient stay prior to sepsis onset (OR 2.90 (1.41-5.94) for &gt;21d versus &lt;4d), were associated with increased 30 day mortality. Being in a surgical or orthopaedic, versus medical, ward was associated with lower mortality (OR 0.47 (0.27-0.81) and 0.26 (0.11-0.63), respectively). Blood culture results (positive vs. negative) were not significantly association with mortality. All severity scores predicted mortality but performance varied. The CURB65 community-acquired pneumonia severity score had the best performance characteristics (sensitivity 81%, specificity 52%, positive predictive value 29%, negative predictive value 92%, for 30 day mortality), including having the largest AUROC curve (0.72, 95% CI 0.67-0.77). Conclusions: The CURB65 pneumonia severity score outperformed five other severity scores in predicting risk of death among a cohort of hospital inpatients with sepsis. The utility of the CURB65 score for risk-stratifying patients with sepsis in clinical practice will depend on replicating these findings in a validation cohort including patients with sepsis on admission to hospital

Community antibiotic prescribing in patients with COVID-19 across three pandemic waves:a population-based study in Scotland, UK

Objectives: This study aims to examine community antibiotic prescribing across a complete geographical area for people with a positive COVID-19 test across three pandemic waves, and to examine health and demographic factors associated with antibiotic prescribing.Design: A population-based study using administrative data.Setting: A complete geographical region within Scotland, UK.Participants: Residents of two National Health Service Scotland health boards with SARS-CoV-2 virus test results from 1 February 2020 to 31 March 2022 (n=184 954). Individuals with a positive test result (n=16 025) had data linked to prescription and hospital admission data ±28 days of the test, general practice data for high-risk comorbidities and demographic data.Outcome measures: The associations between patient factors and the odds of antibiotic prescription in COVID-19 episodes across three pandemic waves from multivariate binary logistic regression.Results: Data included 768 206 tests for 184 954 individuals, identifying 16 240 COVID-19 episodes involving 16 025 individuals. There were 3263 antibiotic prescriptions ±28 days for 2395 episodes. 35.6% of episodes had a prescription only before the test date, 52.3% of episodes after and 12.1% before and after. Antibiotic prescribing reduced over time: 20.4% of episodes in wave 1, 17.7% in wave 2 and 12.0% in wave 3. In multivariate logistic regression, being female (OR 1.31, 95% CI 1.19 to 1.45), older (OR 3.02, 95% CI 2.50 to 3.68 75+ vs &lt;25 years), having a high-risk comorbidity (OR 1.45, 95% CI 1.31 to 1.61), a hospital admission ±28 days of an episode (OR 1.58, 95% CI 1.42 to 1.77) and health board region (OR 1.14, 95% CI 1.03 to 1.25, board B vs A) increased the odds of receiving an antibiotic.Conclusion: Community antibiotic prescriptions in COVID-19 episodes were uncommon in this population and likelihood was associated with patient factors. The reduction over pandemic waves may represent increased knowledge regarding COVID-19 treatment and/or evolving symptomatology

Community antibiotic prescribing in patients with COVID-19 across three pandemic waves:a population-based study in Scotland, UK

Objectives: This study aims to examine community antibiotic prescribing across a complete geographical area for people with a positive COVID-19 test across three pandemic waves, and to examine health and demographic factors associated with antibiotic prescribing.Design: A population-based study using administrative data.Setting: A complete geographical region within Scotland, UK.Participants: Residents of two National Health Service Scotland health boards with SARS-CoV-2 virus test results from 1 February 2020 to 31 March 2022 (n=184 954). Individuals with a positive test result (n=16 025) had data linked to prescription and hospital admission data ±28 days of the test, general practice data for high-risk comorbidities and demographic data.Outcome measures: The associations between patient factors and the odds of antibiotic prescription in COVID-19 episodes across three pandemic waves from multivariate binary logistic regression.Results: Data included 768 206 tests for 184 954 individuals, identifying 16 240 COVID-19 episodes involving 16 025 individuals. There were 3263 antibiotic prescriptions ±28 days for 2395 episodes. 35.6% of episodes had a prescription only before the test date, 52.3% of episodes after and 12.1% before and after. Antibiotic prescribing reduced over time: 20.4% of episodes in wave 1, 17.7% in wave 2 and 12.0% in wave 3. In multivariate logistic regression, being female (OR 1.31, 95% CI 1.19 to 1.45), older (OR 3.02, 95% CI 2.50 to 3.68 75+ vs &lt;25 years), having a high-risk comorbidity (OR 1.45, 95% CI 1.31 to 1.61), a hospital admission ±28 days of an episode (OR 1.58, 95% CI 1.42 to 1.77) and health board region (OR 1.14, 95% CI 1.03 to 1.25, board B vs A) increased the odds of receiving an antibiotic.Conclusion: Community antibiotic prescriptions in COVID-19 episodes were uncommon in this population and likelihood was associated with patient factors. The reduction over pandemic waves may represent increased knowledge regarding COVID-19 treatment and/or evolving symptomatology

Data linkage and statistical modelling to provide stratified risk assessment for HAI

Objectives: The use of “real-time” data to support individual patient management and outcome assessment requires the development of risk assessment models. This could be delivered through a learning health system by the building robust statistical analysis tools onto the existing linked data held by NHS Scotland’s Infection Intelligence Platform (IIP) and developed within the Scottish Healthcare Associated Infection Prevention Institute (SHAIPI). This project will create prediction models for the risk of acquiring a healthcare associated infection (HAI), and particular outcomes, at the point of GP consultation/ hospital admission which could aid clinical decision making. Approach: We demonstrate the capability using the HAI Clostridium difficile (CDI) from 2010-2013. Using linked national individual level data on community prescribing, hospitalisations, infections and death records we extracted all cases of CDI and by comparing to matched population-based controls, examined the impact of prior hospital admissions, care home residence, comorbidities, exposure to gastric acid suppressive drugs and antibiotic exposure, defined as both cumulative (total defined daily dose (DDD)) and temporal antimicrobial exposure in the previous 6 months, to the risk of CDI acquisition. Antimicrobial exposure was considered for all drugs and the higher risk broad spectrum antibiotics (4Cs). Associations are assessed using conditional logistic regression. Using cross-validation we assess the ability of the model to accurately predict CDI infection. Risk scores for acquisition of CDI are estimated by combining these predictions with age and gender population incidence. Results: In the period 2010-2013 there were 1446 cases of CDI with matched 7964 controls. A significant dose-response relationship for exposure to any antimicrobial (1-7 DDDs OR=2.3 rising to OR=4.4 for 29+ DDDs) and, with elevated risk, to the 4C group (1-7 DDDs OR=3.8 rising to OR=17.9 for 29+ DDDs). Exposure elevates CDI risk most in the month after prescription but for 4C antimicrobials the elevated risk remains 6 months later (4C OR=12.4 within 1 month, OR=2.6 4-6 months later). The risk of CDI was also increased with more co-morbidities, previous hospitalisations, care home residency, increased number of prescriptions, and gastric acid suppression. Conclusion: Despite limitations to current application in practice,(paucity of patient level in-hospital prescribing data and constraints of the timeliness of the data), when fully developed this system will enable risk classification to identify patients most at risk of HAI and adverse outcomes to aid clinical decision making

Data linkage and statistical modelling to provide stratified risk assessment for HAI

Objectives: The use of “real-time” data to support individual patient management and outcome assessment requires the development of risk assessment models. This could be delivered through a learning health system by the building robust statistical analysis tools onto the existing linked data held by NHS Scotland’s Infection Intelligence Platform (IIP) and developed within the Scottish Healthcare Associated Infection Prevention Institute (SHAIPI). This project will create prediction models for the risk of acquiring a healthcare associated infection (HAI), and particular outcomes, at the point of GP consultation/ hospital admission which could aid clinical decision making. Approach: We demonstrate the capability using the HAI Clostridium difficile (CDI) from 2010-2013. Using linked national individual level data on community prescribing, hospitalisations, infections and death records we extracted all cases of CDI and by comparing to matched population-based controls, examined the impact of prior hospital admissions, care home residence, comorbidities, exposure to gastric acid suppressive drugs and antibiotic exposure, defined as both cumulative (total defined daily dose (DDD)) and temporal antimicrobial exposure in the previous 6 months, to the risk of CDI acquisition. Antimicrobial exposure was considered for all drugs and the higher risk broad spectrum antibiotics (4Cs). Associations are assessed using conditional logistic regression. Using cross-validation we assess the ability of the model to accurately predict CDI infection. Risk scores for acquisition of CDI are estimated by combining these predictions with age and gender population incidence. Results: In the period 2010-2013 there were 1446 cases of CDI with matched 7964 controls. A significant dose-response relationship for exposure to any antimicrobial (1-7 DDDs OR=2.3 rising to OR=4.4 for 29+ DDDs) and, with elevated risk, to the 4C group (1-7 DDDs OR=3.8 rising to OR=17.9 for 29+ DDDs). Exposure elevates CDI risk most in the month after prescription but for 4C antimicrobials the elevated risk remains 6 months later (4C OR=12.4 within 1 month, OR=2.6 4-6 months later). The risk of CDI was also increased with more co-morbidities, previous hospitalisations, care home residency, increased number of prescriptions, and gastric acid suppression. Conclusion: Despite limitations to current application in practice,(paucity of patient level in-hospital prescribing data and constraints of the timeliness of the data), when fully developed this system will enable risk classification to identify patients most at risk of HAI and adverse outcomes to aid clinical decision making