Correction: Development and Validation of a Clinical Prediction Rule for Bacteremia among Maintenance Hemodialysis Patients in Outpatient Settings.
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ABSTRACT:
SUBMITTER: Sasaki S
PROVIDER: S-EPMC5513555 | biostudies-other | 2017
REPOSITORIES: biostudies-other
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Project description:BackgroundTo our knowledge, no reliable clinical prediction rule (CPR) for identifying bacteremia in hemodialysis (HD) patients has been established. The aim of this study was to develop a CPR for bacteremia in maintenance HD patients visiting the outpatient department.MethodsThis multicenter cohort study involved consecutive maintenance HD patients who visited the outpatient clinic or emergency room of seven Japanese institutions between August 2011 and July 2013. The outcome measure was bacteremia diagnosed based on the results of blood cultures. The candidate predictors for bacteremia were extracted through a literature review. A CPR for bacteremia was developed using a coefficient-based multivariable logistic regression scoring method, and calibration was performed. The test performance was then assessed for the CPR.ResultsOf 507 patients eligible for the study, we analyzed the 293 with a complete dataset for candidate predictors. Of these 293 patients, 48 (16.4%) were diagnosed with bacteremia. At the conclusion of the deviation process, body temperature ? 38.3°C, heart rate ? 125 /min, C-reactive protein ? 10 mg/dL, alkaline phosphatase >360 IU/L, and no prior antibiotics use within the past week were retained and scored. The CPR had a good fit for the model on calibration. The AUC of the CPR was 0.76, and for score CPR ? 2, the sensitivity and specificity were 89.6% and 51.4%, respectively.ConclusionsWe established a simple CPR for bacteremia in maintenance HD patients using routinely obtained clinical information in an outpatient setting. This model may facilitate more appropriate clinical decision making.
| S-EPMC5231279 | biostudies-literature
Project description:IntroductionHaving developed a clinical prediction rule (CPR) for bacteremia among hemodialysis (HD) outpatients (BAC-HD score), we performed external validation.Materials & methodsData were collected on maintenance HD patients at two Japanese tertiary-care hospitals from January 2013 to December 2015. We enrolled 429 consecutive patients (aged ≥ 18 y) on maintenance HD who had had two sets of blood cultures drawn on admission to assess for bacteremia. We validated the predictive ability of the CPR using two validation cohorts. Index tests were the BAC-HD score and a CPR developed by Shapiro et al. The outcome was bacteremia, based on the results of the admission blood cultures. For added value, we also measured changes in the area under the receiver operating characteristic curve (AUC) using logistic regression and Net Reclassification Improvement (NRI), in which each CPR was added to the basic model.ResultsIn Validation cohort 1 (360 subjects), compared to a Model 1 (Basic Model) AUC of 0.69 (95% confidence interval [95% CI]: 0.59-0.80), the AUC of Model 2 (Basic model + BAC-HD score) and Model 3 (Basic model + Shapiro's score) increased to 0.8 (95% CI: 0.71-0.88) and 0.73 (95% CI: 0.63-0.83), respectively. In validation cohort 2 (96 subjects), compared to a Model 1 AUC of 0.81 (95% CI: 0.68-0.94), the AUCs of Model 2 and Model 3 increased to 0.83 (95% CI: 0.72-0.95) and 0.85 (95% CI: 0.76-0.94), respectively. NRIs on addition of the BAC-HD score and Shapiro's score were 0.3 and 0.06 in Validation cohort 1, and 0.27 and 0.13, respectively, in Validation cohort 2.ConclusionEither the BAC-HD score or Shapiro's score may improve the ability to diagnose bacteremia in HD patients. Reclassification was better with the BAC-HD score.
| S-EPMC7899347 | biostudies-literature
Project description:Clinicians have difficulty predicting need for hospitalization of children with acute asthma exacerbations.The objective of this study was to develop and internally validate a multivariable asthma prediction rule (APR) to inform hospitalization decision making in children aged 5-17 years with acute asthma exacerbations.Between April 2008 and February 2013 we enrolled a prospective cohort of patients aged 5-17 years with asthma who presented to our pediatric emergency department with acute exacerbations. Predictors for APR modeling included 15 demographic characteristics, asthma chronic control measures, and pulmonary examination findings in participants at the time of triage and before treatment. The primary outcome variable for APR modeling was need for hospitalization (length of stay >24 h for those admitted to hospital or relapse for those discharged). A secondary outcome was the hospitalization decision of the clinical team. We used penalized maximum likelihood multiple logistic regression modeling to examine the adjusted association of each predictor variable with the outcome. Backward step-down variable selection techniques were used to yield reduced-form models.Data from 928 of 933 participants were used for prediction rule modeling, with median [interquartile range] age 8.8 [6.9, 11.2] years, 61% male, and 59% African-American race. Both full (penalized) and reduced-form models for each outcome calibrated well, with bootstrap-corrected c-indices of 0.74 and 0.73 for need for hospitalization and 0.81 in each case for hospitalization decision.The APR predicts the need for hospitalization of children with acute asthma exacerbations using predictor variables available at the time of presentation to an emergency department.
| S-EPMC4355052 | biostudies-other
Project description:OBJECTIVE:In cases of community acquired pneumonia (CAP), it has been known that blood cultures have low yields and rarely affect clinical outcomes. Despite many studies predicting the likelihood of bacteremia in CAP patients, those results have been rarely implemented in clinical practice, and use of blood culture in CAP is still increasing. This study evaluated impact of implementing a previously derived and validated bacteremia prediction rule. METHODS:In this registry-based before and after study, we used piecewise regression analysis to compare the blood culture rate before and after implementation of the prediction rule. We also compared 30-day mortality, emergency department (ED) length of stay, time-interval to initial antibiotics after ED arrival, and any changes to the antibiotics regimen as results of the blood cultures. In subgroup analysis, we compared two groups (with or without the use of the prediction rule) after implementation period, using propensity score matching. RESULTS:Following the implementation, the blood culture rate declined from 85.5% to 78.1% (P=0.003) without significant changes in 30-day mortality and antibiotics regimen. The interval to initial antibiotics (231min vs. 221min, P=0.362) and length of stay (1019min vs. 954min, P=0.354) were not significantly changed. In subgroup analysis, the group that use the prediction rule showed 25min faster antibiotics initiation (P=0.002) and 48min shorter length of stay (P=0.007) than the group that did not use the rule. CONCLUSION:Implementation of the bacteremia prediction rule in CAP patients reduced the blood culture rate without affecting the 30-day mortality and antibiotics regimen.
| S-EPMC7127687 | biostudies-literature
Project description:BackgroundExperiences from the first wave of the 2019 coronavirus disease (COVID-19) pandemic can aid in the development of future preventive strategies. To date, risk prediction models for COVID-19-related incidence and outcomes in hemodialysis (HD) patients are missing.MethodsWe developed risk prediction models for COVID-19 incidence and mortality among HD patients. We studied 38 256 HD patients from a multi-national dialysis cohort between 3 March and 3 July 2020. Risk prediction models were developed and validated, based on predictors readily available in outpatient HD units. We compared mortality among patients with and without COVID-19, matched for age, sex and diabetes.ResultsDuring the observational period, 1259 patients (3.3%) acquired COVID-19. Of these, 62% were hospitalized or died. Mortality was 22% among COVID-19 patients with odds ratios 219.8 [95% confidence interval (CI) 80.6-359] to 342.7 (95% CI 60.6-13 595.1), compared to matched patients without COVID-19. Since the first wave of the pandemic affected most European countries during the study, the risk prediction model for incidence of COVID-19 was developed and validated in European patients only [N = 22 826 area under the ROC curve(AUC)Dev 0.64, AUCVal 0.69]. The model for prediction of mortality was developed in all COVID-19 patients (AUCDev 0.71, AUCVal 0.78). Angiotensin receptor blockers were independently associated with a lower incidence of COVID-19 in European patients.ConclusionsWe identified modifiable risk factors for COVID-19 incidence and outcome in HD patients. Our risk prediction tools can be readily applied in clinical practice. This can aid in the development of preventive strategies for future waves of COVID-19.
| S-EPMC7929029 | biostudies-literature
Project description:Assessment of suicide risk in individuals with severe mental illness is currently inconsistent, and based on clinical decision-making with or without tools developed for other purposes. We aimed to develop and validate a predictive model for suicide using data from linked population-based registers in individuals with severe mental illness. A national cohort of 75,158 Swedish individuals aged 15-65 with a diagnosis of severe mental illness (schizophrenia-spectrum disorders, and bipolar disorder) with 574,018 clinical patient episodes between 2001 and 2008, split into development (58,771 patients, 494 suicides) and external validation (16,387 patients, 139 suicides) samples. A multivariable derivation model was developed to determine the strength of pre-specified routinely collected socio-demographic and clinical risk factors, and then tested in external validation. We measured discrimination and calibration for prediction of suicide at 1 year using specified risk cut-offs. A 17-item clinical risk prediction model for suicide was developed and showed moderately good measures of discrimination (c-index 0.71) and calibration. For risk of suicide at 1 year, using a pre-specified 1% cut-off, sensitivity was 55% (95% confidence interval [CI] 47-63%) and specificity was 75% (95% CI 74-75%). Positive and negative predictive values were 2% and 99%, respectively. The model was used to generate a simple freely available web-based probability-based risk calculator (Oxford Mental Illness and Suicide tool or OxMIS) without categorical cut-offs. A scalable prediction score for suicide in individuals with severe mental illness is feasible. If validated in other samples and linked to effective interventions, using a probability score may assist clinical decision-making.
| S-EPMC6389890 | biostudies-literature
Project description:Background and objectivesThe risk assessment for developing ESRD remains limited in patients with IgA nephropathy (IgAN). The aim of this study was to develop and validate a prediction rule for estimating the individual risk of ESRD in patients with IgAN.Design, setting, participants, & measurementsA total of 698 patients with IgAN diagnosed by renal biopsy at Kyushu University Hospital (derivation cohort) between 1982 and 2010 were retrospectively followed. The Oxford classification was used to evaluate the pathologic lesions. The risk factors for developing ESRD were evaluated using a Cox proportional hazard model with a stepwise backward elimination method. The prediction rule was verified using data from 702 patients diagnosed at Japanese Red Cross Fukuoka Hospital (validation cohort) between 1979 and 2002.ResultsIn the derivation cohort, 73 patients developed ESRD during the median 4.7-year follow-up. The final prediction model included proteinuria (hazard ratio [HR], 1.30; 95% confidence interval [95% CI], 1.16 to 1.45, every 1 g/24 hours), estimated GFR (HR, 0.84; 95% CI, 0.74 to 0.96, every 10 ml/min per 1.73 m(2)), mesangial proliferation (HR, 1.85; 95% CI, 1.10 to 3.11), segmental sclerosis (HR, 3.21; 95% CI, 1.37 to 7.51), and interstitial fibrosis/tubular atrophy (T1: HR, 5.30; 95% CI, 2.63 to 10.7; T2: HR, 20.5; 95% CI, 9.05 to 46.5) as independent risk factors for developing ESRD. To create a prediction rule, the score for each variable was weighted by the regression coefficients calculated using the relevant Cox model. The incidence of ESRD increased linearly with increases in the total risk scores (P for trend <0.001). Furthermore, the prediction rule demonstrated good discrimination (c-statistic=0.89) and calibration (Hosmer-Lemeshow test, P=0.78) in the validation cohort.ConclusionsThis study developed and validated a new prediction rule using clinical measures and the Oxford classification for developing ESRD in patients with IgAN.
| S-EPMC3848397 | biostudies-literature
Project description:Aims/hypothesisMicroalbuminuria is common in type 1 diabetes and is associated with an increased risk of renal and cardiovascular disease. We aimed to develop and validate a clinical prediction rule that estimates the absolute risk of microalbuminuria.MethodsData from the European Diabetes Prospective Complications Study (n = 1115) were used to develop the prediction rule (development set). Multivariable logistic regression analysis was used to assess the association between potential predictors and progression to microalbuminuria within 7 years. The performance of the prediction rule was assessed with calibration and discrimination (concordance statistic [c-statistic]) measures. The rule was validated in three other diabetes studies (Pittsburgh Epidemiology of Diabetes Complications [EDC] study, Finnish Diabetic Nephropathy [FinnDiane] study and Coronary Artery Calcification in Type 1 Diabetes [CACTI] study).ResultsOf patients in the development set, 13% were microalbuminuric after 7 years. Glycosylated haemoglobin, AER, WHR, BMI and ever smoking were found to be the most important predictors. A high-risk group (n = 87 [8%]) was identified with a risk of progression to microalbuminuria of 32%. Predictions showed reasonable discriminative ability, with c-statistic of 0.71. The rule showed good calibration and discrimination in EDC, FinnDiane and CACTI (c-statistic 0.71, 0.79 and 0.79, respectively).Conclusions/interpretationWe developed and validated a clinical prediction rule that uses relatively easily obtainable patient characteristics to predict microalbuminuria in patients with type 1 diabetes. This rule can help clinicians to decide on more frequent check-ups for patients at high risk of microalbuminuria in order to prevent long-term chronic complications.
| S-EPMC2797626 | biostudies-literature
Project description:BackgroundThis study aimed to assess the situational capacity for nutrition care delivery in the outpatient hemodialysis (HD) setting in Malaysia by evaluating dietitian accessibility, nutrition practices and patients' outcomes.MethodsA 17-item questionnaire was developed to assess nutrition practices and administered to dialysis managers of 150 HD centers, identified through the National Renal Registry. Nutritional outcomes of 4362 patients enabled crosscutting comparisons as per dietitian accessibility and center sector.ResultsDedicated dietitian (18%) and visiting/shared dietitian (14.7%) service availability was limited, with greatest accessibility at government centers (82.4%)?>?non-governmental organization (NGO) centers (26.7%)?>?private centers (15.1%). Nutritional monitoring varied across HD centers as per albumin (100%)?>?normalized protein catabolic rate (32.7%)?>?body mass index (BMI, 30.7%)?>?dietary intake (6.0%). Both sector and dietitian accessibility was not associated with achieving albumin ?40?g/L. However, NGO centers were 36% more likely (p?=?0.030) to achieve pre-dialysis serum creatinine ?884??mol/L compared to government centers, whilst centers with dedicated dietitian service were 29% less likely (p?=?0.017) to achieve pre-dialysis serum creatinine ?884??mol/L. In terms of BMI, private centers were 32% more likely (p?=?0.022) to achieve BMI???25.0?kg/m2 compared to government centers. Private centers were 62% less likely (p?<? 0.001) while NGO centers were 56% less likely (p?<? 0.001) to achieve serum phosphorus control compared to government centers. Patients from centers with a shared/visiting dietitian had 35% lower probability (p?<? 0.001) to achieve serum phosphorus levels below 1.78?mmol/L compared to centers without access to a dietitian.ConclusionsThere were clear discrepancies in nutritional care in Malaysian HD centers. Changes in stakeholder policy are required to ensure that dietitian service is available in Malaysian HD centers.
| S-EPMC6280465 | biostudies-literature
Project description:BackgroundChest pain can be caused by various conditions, with life-threatening cardiac disease being of greatest concern. Prediction scores to rule out coronary artery disease have been developed for use in emergency settings. We developed and validated a simple prediction rule for use in primary care.MethodsWe conducted a cross-sectional diagnostic study in 74 primary care practices in Germany. Primary care physicians recruited all consecutive patients who presented with chest pain (n = 1249) and recorded symptoms and findings for each patient (derivation cohort). An independent expert panel reviewed follow-up data obtained at six weeks and six months on symptoms, investigations, hospital admissions and medications to determine the presence or absence of coronary artery disease. Adjusted odds ratios of relevant variables were used to develop a prediction rule. We calculated measures of diagnostic accuracy for different cut-off values for the prediction scores using data derived from another prospective primary care study (validation cohort).ResultsThe prediction rule contained five determinants (age/sex, known vascular disease, patient assumes pain is of cardiac origin, pain is worse during exercise, and pain is not reproducible by palpation), with the score ranging from 0 to 5 points. The area under the curve (receiver operating characteristic curve) was 0.87 (95% confidence interval [CI] 0.83-0.91) for the derivation cohort and 0.90 (95% CI 0.87-0.93) for the validation cohort. The best overall discrimination was with a cut-off value of 3 (positive result 3-5 points; negative result <or= 2 points), which had a sensitivity of 87.1% (95% CI 79.9%-94.2%) and a specificity of 80.8% (77.6%-83.9%).InterpretationThe prediction rule for coronary artery disease in primary care proved to be robust in the validation cohort. It can help to rule out coronary artery disease in patients presenting with chest pain in primary care.
| S-EPMC2934794 | biostudies-literature