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1.
BMC Med Inform Decis Mak ; 20(1): 60, 2020 Mar 30.
Artigo em Inglês | MEDLINE | ID: mdl-32228556

RESUMO

BACKGROUND: The rapid adoption of electronic health records (EHRs) holds great promise for advancing medicine through practice-based knowledge discovery. However, the validity of EHR-based clinical research is questionable due to poor research reproducibility caused by the heterogeneity and complexity of healthcare institutions and EHR systems, the cross-disciplinary nature of the research team, and the lack of standard processes and best practices for conducting EHR-based clinical research. METHOD: We developed a data abstraction framework to standardize the process for multi-site EHR-based clinical studies aiming to enhance research reproducibility. The framework was implemented for a multi-site EHR-based research project, the ESPRESSO project, with the goal to identify individuals with silent brain infarctions (SBI) at Tufts Medical Center (TMC) and Mayo Clinic. The heterogeneity of healthcare institutions, EHR systems, documentation, and process variation in case identification was assessed quantitatively and qualitatively. RESULT: We discovered a significant variation in the patient populations, neuroimaging reporting, EHR systems, and abstraction processes across the two sites. The prevalence of SBI for patients over age 50 for TMC and Mayo is 7.4 and 12.5% respectively. There is a variation regarding neuroimaging reporting where TMC are lengthy, standardized and descriptive while Mayo's reports are short and definitive with more textual variations. Furthermore, differences in the EHR system, technology infrastructure, and data collection process were identified. CONCLUSION: The implementation of the framework identified the institutional and process variations and the heterogeneity of EHRs across the sites participating in the case study. The experiment demonstrates the necessity to have a standardized process for data abstraction when conducting EHR-based clinical studies.

3.
Cancer Med ; 9(4): 1462-1472, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-31899856

RESUMO

BACKGROUND: As there are few validated tools to identify treatment-related adverse events across cancer care settings, we sought to develop oncology-specific "triggers" to flag potential adverse events among cancer patients using claims data. METHODS: 322 887 adult patients undergoing an initial course of cancer-directed therapy for breast, colorectal, lung, or prostate cancer from 2008 to 2014 were drawn from a large commercial claims database. We defined 16 oncology-specific triggers using diagnosis and procedure codes. To distinguish treatment-related complications from comorbidities, we required a logical and temporal relationship between a treatment and the associated trigger. We tabulated the prevalence of triggers by cancer type and metastatic status during 1-year of follow-up, and examined cancer trigger risk factors. RESULTS: Cancer-specific trigger events affected 19% of patients over the initial treatment year. The trigger burden varied by disease and metastatic status, from 6% of patients with nonmetastatic prostate cancer to 41% and 50% of those with metastatic colorectal and lung cancers, respectively. The most prevalent triggers were abnormal serum bicarbonate, blood transfusion, non-contrast chest CT scan following radiation therapy, and hypoxemia. Among patients with metastatic disease, 10% had one trigger event and 29% had two or more. Triggers were more common among older patients, women, non-whites, patients with low family incomes, and those without a college education. CONCLUSIONS: Oncology-specific triggers offer a promising method for identifying potential patient safety events among patients across cancer care settings.

4.
Epidemiology ; 31(3): e30-e31, 2020 May.
Artigo em Inglês | MEDLINE | ID: mdl-31880640
5.
Ann Intern Med ; 2019 Nov 12.
Artigo em Inglês | MEDLINE | ID: mdl-31711094

RESUMO

The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed to promote the conduct of, and provide guidance for, predictive analyses of heterogeneity of treatment effects (HTE) in clinical trials. The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risk with versus without the intervention, taking into account all relevant patient attributes simultaneously, to support more personalized clinical decision making than can be made on the basis of only an overall average treatment effect. The authors distinguished 2 categories of predictive HTE approaches (a "risk-modeling" and an "effect-modeling" approach) and developed 4 sets of guidance statements: criteria to determine when risk-modeling approaches are likely to identify clinically meaningful HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. They discuss limitations of these methods and enumerate research priorities for advancing methods designed to generate more personalized evidence. This explanation and elaboration document describes the intent and rationale of each recommendation and discusses related analytic considerations, caveats, and reservations.

6.
Ann Intern Med ; 2019 Nov 12.
Artigo em Inglês | MEDLINE | ID: mdl-31711134

RESUMO

Heterogeneity of treatment effect (HTE) refers to the nonrandom variation in the magnitude or direction of a treatment effect across levels of a covariate, as measured on a selected scale, against a clinical outcome. In randomized controlled trials (RCTs), HTE is typically examined through a subgroup analysis that contrasts effects in groups of patients defined "1 variable at a time" (for example, male vs. female or old vs. young). The authors of this statement present guidance on an alternative approach to HTE analysis, "predictive HTE analysis." The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risks with versus without the intervention, taking into account all relevant patient attributes simultaneously. The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed using a multidisciplinary technical expert panel, targeted literature reviews, simulations to characterize potential problems with predictive approaches, and a deliberative process engaging the expert panel. The authors distinguish 2 categories of predictive HTE approaches: a "risk-modeling" approach, wherein a multivariable model predicts the risk for an outcome and is applied to disaggregate patients within RCTs to define risk-based variation in benefit, and an "effect-modeling" approach, wherein a model is developed on RCT data by incorporating a term for treatment assignment and interactions between treatment and baseline covariates. Both approaches can be used to predict differential absolute treatment effects, the most relevant scale for clinical decision making. The authors developed 4 sets of guidance: criteria to determine when risk-modeling approaches are likely to identify clinically important HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. The PATH Statement, together with its explanation and elaboration document, may guide future analyses and reporting of RCTs.

7.
J Am Heart Assoc ; 8(20): e011972, 2019 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-31583938

RESUMO

Background While many clinical prediction models (CPMs) exist to guide valvular heart disease treatment decisions, the relative performance of these CPMs is largely unknown. We systematically describe the CPMs available for patients with valvular heart disease with specific attention to performance in external validations. Methods and Results A systematic review identified 49 CPMs for patients with valvular heart disease treated with surgery (n=34), percutaneous interventions (n=12), or no intervention (n=3). There were 204 external validations of these CPMs. Only 35 (71%) CPMs have been externally validated. Sixty-five percent (n=133) of the external validations were performed on distantly related populations. There was substantial heterogeneity in model performance and a median percentage change in discrimination of -27.1% (interquartile range, -49.4%--5.7%). Nearly two-thirds of validations (n=129) demonstrate at least a 10% relative decline in discrimination. Discriminatory performance of EuroSCORE II and Society of Thoracic Surgeons (2009) models (accounting for 73% of external validations) varied widely: EuroSCORE II validation c-statistic range 0.50 to 0.95; Society of Thoracic Surgeons (2009) Models validation c-statistic range 0.50 to 0.86. These models performed well when tested on related populations (median related validation c-statistics: EuroSCORE II, 0.82 [0.76, 0.85]; Society of Thoracic Surgeons [2009], 0.72 [0.67, 0.79]). There remain few (n=9) external validations of transcatheter aortic valve replacement CPMs. Conclusions Many CPMs for patients with valvular heart disease have never been externally validated and isolated external validations appear insufficient to assess the trustworthiness of predictions. For surgical valve interventions, there are existing predictive models that perform reasonably well on related populations. For transcatheter aortic valve replacement (CPMs additional external validations are needed to broadly understand the trustworthiness of predictions.

8.
J Clin Transl Sci ; 3(1): 27-36, 2019 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-31404154

RESUMO

Background: To enhance enrollment into randomized clinical trials (RCTs), we proposed electronic health record-based clinical decision support for patient-clinician shared decision-making about care and RCT enrollment, based on "mathematical equipoise." Objectives: As an example, we created the Knee Osteoarthritis Mathematical Equipoise Tool (KOMET) to determine the presence of patient-specific equipoise between treatments for the choice between total knee replacement (TKR) and nonsurgical treatment of advanced knee osteoarthritis. Methods: With input from patients and clinicians about important pain and physical function treatment outcomes, we created a database from non-RCT sources of knee osteoarthritis outcomes. We then developed multivariable linear regression models that predict 1-year individual-patient knee pain and physical function outcomes for TKR and for nonsurgical treatment. These predictions allowed detecting mathematical equipoise between these two options for patients eligible for TKR. Decision support software was developed to graphically illustrate, for a given patient, the degree of overlap of pain and functional outcomes between the treatments and was pilot tested for usability, responsiveness, and as support for shared decision-making. Results: The KOMET predictive regression model for knee pain had four patient-specific variables, and an r 2 value of 0.32, and the model for physical functioning included six patient-specific variables, and an r 2 of 0.34. These models were incorporated into prototype KOMET decision support software and pilot tested in clinics, and were generally well received. Conclusions: Use of predictive models and mathematical equipoise may help discern patient-specific equipoise to support shared decision-making for selecting between alternative treatments and considering enrollment into an RCT.

9.
Clin Breast Cancer ; 19(4): 259-267.e1, 2019 08.
Artigo em Inglês | MEDLINE | ID: mdl-31175052

RESUMO

BACKGROUND: Anthracycline agents can cause cardiotoxicity. We used multivariable risk prediction models to identify a subset of patients with breast cancer at high risk of cardiotoxicity, for whom the harms of anthracycline chemotherapy may balance or exceed the benefits. PATIENTS AND METHODS: A clinical prediction model for anthracycline cardiotoxicity was created in 967 patients with human epidermal growth factor receptor-negative breast cancer treated with doxorubicin in the ECOG-ACRIN study E5103. Cardiotoxicity was defined as left ventricular ejection fraction (LVEF) decline of ≥ 10% to < 50% and/or a centrally adjudicated clinical heart failure diagnosis. Patient-specific incremental absolute benefit of anthracyclines (compared with non-anthracycline taxane chemotherapy) was estimated using the PREDICT model to assess breast cancer mortality risk. RESULTS: Of the 967 women who initiated therapy, 51 (5.3%) developed cardiotoxicity (12 with clinical heart failure). In a multivariate model, increasing age (odds ratio [OR], 1.04; 95% confidence interval [CI], 1.01-1.08), higher body mass index (OR, 1.06; 95% CI, 1.02-1.10), and lower baseline LVEF (OR, 0.93; 95% CI, 0.89-0.98) at baseline were significantly associated with cardiotoxicity. The concordance statistic of the risk model was 0.70 (95% CI, 0.63-0.77). In patients with low anticipated treatment benefit (n = 176) from the addition of anthracycline (< 2% absolute risk difference of breast cancer mortality at 10 years), 16 (9%) of 176 had a > 10% risk of cardiotoxicity and 61 (35%) of 176 had a 5% to 10% risk of cardiotoxicity at 1 year. CONCLUSION: Older age, higher body mass index, and lower baseline LVEF were associated with increased risk of cardiotoxicity. We identified a subgroup with low predicted absolute benefit of anthracyclines but with high predicted risk of cardiotoxicity. Additional studies are needed incorporating long-term cardiac outcomes and cardiotoxicity model external validation prior to implementation in routine clinical practice.

10.
J Clin Epidemiol ; 114: 72-83, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31195109

RESUMO

OBJECTIVES: We aimed to compare the performance of different regression modeling approaches for the prediction of heterogeneous treatment effects. STUDY DESIGN AND SETTING: We simulated trial samples (n = 3,600; 80% power for a treatment odds ratio of 0.8) from a superpopulation (N = 1,000,000) with 12 binary risk predictors, both without and with six true treatment interactions. We assessed predictions of treatment benefit for four regression models: a "risk model" (with a constant effect of treatment assignment) and three "effect models" (including interactions of risk predictors with treatment assignment). Three novel performance measures were evaluated: calibration for benefit (i.e., observed vs. predicted risk difference in treated vs. untreated), discrimination for benefit, and prediction error for benefit. RESULTS: The risk modeling approach was well-calibrated for benefit, whereas effect models were consistently overfit, even with doubled sample sizes. Penalized regression reduced miscalibration of the effect models considerably. In terms of discrimination and prediction error, the risk modeling approach was superior in the absence of true treatment effect interactions, whereas penalized regression was optimal in the presence of true treatment interactions. CONCLUSION: A risk modeling approach yields models consistently well calibrated for benefit. Effect modeling may improve discrimination for benefit in the presence of true interactions but is prone to overfitting. Hence, effect models-including only plausible interactions-should be fitted using penalized regression.

11.
JMIR Med Inform ; 7(2): e12109, 2019 Apr 21.
Artigo em Inglês | MEDLINE | ID: mdl-31066686

RESUMO

BACKGROUND: Silent brain infarction (SBI) is defined as the presence of 1 or more brain lesions, presumed to be because of vascular occlusion, found by neuroimaging (magnetic resonance imaging or computed tomography) in patients without clinical manifestations of stroke. It is more common than stroke and can be detected in 20% of healthy elderly people. Early detection of SBI may mitigate the risk of stroke by offering preventative treatment plans. Natural language processing (NLP) techniques offer an opportunity to systematically identify SBI cases from electronic health records (EHRs) by extracting, normalizing, and classifying SBI-related incidental findings interpreted by radiologists from neuroimaging reports. OBJECTIVE: This study aimed to develop NLP systems to determine individuals with incidentally discovered SBIs from neuroimaging reports at 2 sites: Mayo Clinic and Tufts Medical Center. METHODS: Both rule-based and machine learning approaches were adopted in developing the NLP system. The rule-based system was implemented using the open source NLP pipeline MedTagger, developed by Mayo Clinic. Features for rule-based systems, including significant words and patterns related to SBI, were generated using pointwise mutual information. The machine learning models adopted convolutional neural network (CNN), random forest, support vector machine, and logistic regression. The performance of the NLP algorithm was compared with a manually created gold standard. The gold standard dataset includes 1000 radiology reports randomly retrieved from the 2 study sites (Mayo and Tufts) corresponding to patients with no prior or current diagnosis of stroke or dementia. 400 out of the 1000 reports were randomly sampled and double read to determine interannotator agreements. The gold standard dataset was equally split to 3 subsets for training, developing, and testing. RESULTS: Among the 400 reports selected to determine interannotator agreement, 5 reports were removed due to invalid scan types. The interannotator agreements across Mayo and Tufts neuroimaging reports were 0.87 and 0.91, respectively. The rule-based system yielded the best performance of predicting SBI with an accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 0.991, 0.925, 1.000, 1.000, and 0.990, respectively. The CNN achieved the best score on predicting white matter disease (WMD) with an accuracy, sensitivity, specificity, PPV, and NPV of 0.994, 0.994, 0.994, 0.994, and 0.994, respectively. CONCLUSIONS: We adopted a standardized data abstraction and modeling process to developed NLP techniques (rule-based and machine learning) to detect incidental SBIs and WMDs from annotated neuroimaging reports. Validation statistics suggested a high feasibility of detecting SBIs and WMDs from EHRs using NLP.

12.
J Patient Rep Outcomes ; 3(1): 23, 2019 Apr 15.
Artigo em Inglês | MEDLINE | ID: mdl-30982930

RESUMO

BACKGROUND: Incidentally discovered silent brain infarcts (id-SBIs) are an understudied condition with probable clinical significance, but it is not known how patients respond to or prioritize this condition. We sought to assess reporting of id-SBIs and how patients approach their diagnosis. METHODS: Patients with id-SBIs were identified from sequential scans between 12/2015-5/2016, were referred by treating clinicians, or self-referred for the study. This study used qualitative semi-structured interviews. Purposeful sampling was used to achieve diversity in acuity, setting, and recruitment strategy. Interviews were audio-recorded and transcribed. A constant comparative method was used to develop a coding schema, find consensus, and iteratively explore emergent themes until thematic saturation was achieved. RESULTS: Only 10 of 102 patients prospectively identified by neuroimaging were informed of the imaging findings. Twelve participants in total were interviewed. Among the study participants, the primary themes were cognitive, emotional, and behavioral responses to diagnostic, prognostic, and therapeutic uncertainty regarding id-SBIs. Clinicians described id-SBIs to participants as an ambiguous condition. Participants feared potential consequences of id-SBIs, including symptomatic stroke, dementia, and disability. Participants attempted to reduce uncertainty with strategies including equating id-SBIs with symptomatic stroke, self-education about stroke, and seeking second opinions. CONCLUSION: Participants considered id-SBIs to be a serious medical condition. Ambiguous counseling by clinicians on id-SBIs provoked or failed to attenuate fear, leading to participants adopting strategies aimed at reducing uncertainty.

13.
Eur Heart J ; 40(16): 1257-1264, 2019 Apr 21.
Artigo em Inglês | MEDLINE | ID: mdl-30875424

RESUMO

AIMS: The Catheter Ablation vs. Antiarrhythmic Drug Therapy for Atrial Fibrillation (CABANA) trial aimed to assess the impact of ablation on morbidity and mortality. This observational study was conducted in parallel to CABANA to assess trial generalizability. METHODS AND RESULTS: Using a large US administrative database, we identified 183 760 patients with atrial fibrillation (AF) treated with ablation or medical therapy (antiarrhythmic or rate control drugs) between 1 August 2009 and 30 April 2016 (CABANA enrolment period). Propensity score weighting was used to balance patients treated with ablation (N = 12 032) or medical therapy alone (N = 171 728) on 90 dimensions. Ablation was associated with a reduction in the composite endpoint of all-cause mortality, stroke, major bleeding, and cardiac arrest [hazard ratio (HR) 0.75, 95% confidence interval (CI) 0.70-0.81; P < 0.001]. The majority of patients (73.8%) were potentially trial eligible; among whom the risk reduction associated with ablation was greatest (HR 0.70, 95% CI 0.63-0.77; P < 0.001). Among the 3.8% of patients who failed to meet the inclusion criterion, i.e. patients under 65 years without stroke risk factors, the event rates were low and there was no significant relationship with ablation (HR 0.67, 95% CI 0.29-1.56; P = 0.35). Among the 22.4% patients who met at least one of the trial exclusion criteria, there was a lesser but statistically significant reduction associated with ablation (HR 0.85, 95% CI 0.75-0.95; P = 0.01). CONCLUSION: In routine clinical care, ablation was associated with a reduction in the primary CABANA composite endpoint of all-cause mortality, stroke, major bleeding, and cardiac arrest, particularly in patients who were eligible for the trial.

14.
Resuscitation ; 139: 308-313, 2019 06.
Artigo em Inglês | MEDLINE | ID: mdl-30836171

RESUMO

AIM: "Early" withdrawal of life support therapies (eWLST) within the first 3 calendar days after resuscitation from cardiac arrest (CA) is discouraged. We evaluated a prospective multicenter registry of patients admitted to hospitals after resuscitation from CA to determine predictors of eWLST and estimate its impact on outcomes. METHODS: CA survivors enrolled from 2012-2017 in the International Cardiac Arrest Registry (INTCAR) were included. We developed a propensity score for eWLST and matched a cohort with similar probabilities of eWLST who received ongoing care. The incidence of good outcome (Cerebral Performance Category of 1 or 2) was measured across deciles of eWLST in the matched cohort. RESULTS: 2688 patients from 24 hospitals were included. Median ischemic time was 20 (IQR 11, 30) minutes, and 1148 (43%) had an initial shockable rhythm. Withdrawal of life support occurred in 1162 (43%) cases, with 459 (17%) classified as eWLST. Older age, initial non-shockable rhythm, increased ischemic time, shock on admission, out-of-hospital arrest, and admission in the United States were each independently associated with eWLST. All patients with eWLST died, while the matched cohort, good outcome occurred in 21% of patients. 19% of patients within the eWLST group were predicted to have a good outcome, had eWLST not occurred. CONCLUSIONS: Early withdrawal of life support occurs frequently after cardiac arrest. Although the mortality of patients matched to those with eWLST was high, these data showed excess mortality with eWLST.

15.
Health Aff (Millwood) ; 38(1): 60-67, 2019 01.
Artigo em Inglês | MEDLINE | ID: mdl-30615528

RESUMO

Because an intervention's clinical benefit depends on who receives it, a key to improving the efficiency of lung cancer screening with low-dose computed tomography (LDCT) is to incentivize its use among the current or former smokers who are most likely to benefit from it. Despite its clinical advantages and cost-effectiveness, only 3.9 percent of the eligible population underwent LDCT screening in 2015. Using individual lung cancer mortality risk, we developed a policy simulation model to explore the potential impact of implementing risk-targeted incentive programs, compared to either implementing untargeted incentive programs or doing nothing. We found that compared to the status quo, an untargeted incentive program that increased overall LDCT screening from 3,900 (baseline) to 10,000 per 100,000 eligible people would save 12,300 life-years and accrue a net monetary benefit (NMB) of $771 million over a lifetime horizon. Increasing screening by the same amount but targeting higher-risk people would yield an additional 2,470-6,600 life-years and an additional $210-$560 million NMB, depending on the extent of the risk-targeting. Risk-targeted incentive programs could include provider-level bonuses, health plan premium subsidies, and smoking cessation programs to maximize their impact. As clinical medicine becomes more personalized, targeting and incentivizing higher-risk people will help enhance population health and economic efficiency.


Assuntos
Análise Custo-Benefício , Detecção Precoce de Câncer/economia , Neoplasias Pulmonares/diagnóstico , Programas de Rastreamento/economia , Motivação , Saúde da População , Humanos , Neoplasias Pulmonares/mortalidade , Anos de Vida Ajustados por Qualidade de Vida , Fatores de Risco , Tomografia Computadorizada por Raios X/métodos
16.
Stroke ; : STROKEAHA118022745, 2018 Dec 10.
Artigo em Inglês | MEDLINE | ID: mdl-30580725

RESUMO

Background and Purpose- The insulin sensitizer, pioglitazone, reduces cardiovascular risk in patients after an ischemic stroke or transient ischemic attack but increases bone fracture risk. We conducted a secondary analysis of the IRIS trial (Insulin Resistance Intervention After Stroke) to assess the effect of pretreatment risk for fracture on the net benefits of pioglitazone therapy. Methods- IRIS was a randomized placebo-controlled trial of pioglitazone that enrolled patients with insulin resistance but without diabetes mellitus within 180 days of an ischemic stroke or transient ischemic attack. Participants were recruited at 179 international centers from February 2005 to January 2013 and followed for a median of 4.8 years. Fracture risk models were developed from patient characteristics at entry. Within fracture risk strata, we quantified the effects of pioglitazone compared with placebo by estimating the relative risks and absolute 5-year risk differences for fracture and stroke or myocardial infarction. Results- The fracture risk model included points for age, race-ethnicity, sex, body mass index, disability, and medications. The relative increment in fracture risk with pioglitazone was similar in the lower (

17.
BMJ ; 363: k4245, 2018 12 10.
Artigo em Inglês | MEDLINE | ID: mdl-30530757

RESUMO

The use of evidence from clinical trials to support decisions for individual patients is a form of "reference class forecasting": implicit predictions for an individual are made on the basis of outcomes in a reference class of "similar" patients treated with alternative therapies. Evidence based medicine has generally emphasized the broad reference class of patients qualifying for a trial. Yet patients in a trial (and in clinical practice) differ from one another in many ways that can affect the outcome of interest and the potential for benefit. The central goal of personalized medicine, in its various forms, is to narrow the reference class to yield more patient specific effect estimates to support more individualized clinical decision making. This article will review fundamental conceptual problems with the prediction of outcome risk and heterogeneity of treatment effect (HTE), as well as the limitations of conventional (one-variable-at-a-time) subgroup analysis. It will also discuss several regression based approaches to "predictive" heterogeneity of treatment effect analysis, including analyses based on "risk modeling" (such as stratifying trial populations by their risk of the primary outcome or their risk of serious treatment-related harms) and analysis based on "effect modeling" (which incorporates modifiers of relative effect). It will illustrate these approaches with clinical examples and discuss their respective strengths and vulnerabilities.


Assuntos
Medicina Baseada em Evidências , Medicina de Precisão/métodos , Tomada de Decisão Clínica , Humanos , Ensaios Clínicos Controlados Aleatórios como Assunto , Projetos de Pesquisa , Medição de Risco
19.
J Pediatr ; 201: 160-165.e1, 2018 10.
Artigo em Inglês | MEDLINE | ID: mdl-29954609

RESUMO

OBJECTIVE: To examine the external validity of a well-known congenital diaphragmatic hernia (CDH) clinical prediction model using a population-based cohort. STUDY DESIGN: Newborns with CDH born in California between 2007 and 2012 were extracted from the Vital Statistics and Patient Discharge Data Linked Files. The total CDH risk score was calculated according to the Congenital Diaphragmatic Hernia Study Group (CDHSG) model using 5 independent predictors: birth weight, 5-minute Apgar, pulmonary hypertension, major cardiac defects, and chromosomal anomalies. CDHSG model performance on our cohort was validated for discrimination and calibration. RESULTS: A total of 705 newborns with CDH were extracted from 3 213 822 live births. Newborns with CDH were delivered in 150 different hospitals, whereas only 28 hospitals performed CDH repairs (1-85 repairs per hospital). The observed mortality for low-, intermediate-, and high-risk groups were 7.7%, 34.3%, and 54.7%, and predicted mortality for these groups were 4.0%, 23.2%, and 58.5%. The CDHSG model performed well within our cohort with a c-statistic of 0.741 and good calibration. CONCLUSIONS: We successfully validated the CDHSG prediction model using an external population-based cohort of newborns with CDH in California. This cohort may be used to investigate hospital volume-outcome relationships and guide policy development.


Assuntos
Hérnias Diafragmáticas Congênitas/epidemiologia , Vigilância da População , Medição de Risco/métodos , California/epidemiologia , Feminino , Seguimentos , Hérnias Diafragmáticas Congênitas/diagnóstico , Humanos , Incidência , Lactente , Mortalidade Infantil/tendências , Recém-Nascido , Masculino , Reprodutibilidade dos Testes , Estudos Retrospectivos , Taxa de Sobrevida/tendências
20.
BMJ Open ; 8(5): e017641, 2018 05 26.
Artigo em Inglês | MEDLINE | ID: mdl-29804057

RESUMO

OBJECTIVE: Individual patients with the same condition may respond differently to similar treatments. Our aim is to summarise the reporting of person-level heterogeneity of treatment effects (HTE) in multiperson N-of-1 studies and to examine the evidence for person-level HTE through reanalysis. STUDY DESIGN: Systematic review and reanalysis of multiperson N-of-1 studies. DATA SOURCES: Medline, Cochrane Controlled Trials, EMBASE, Web of Science and review of references through August 2017 for N-of-1 studies published in English. STUDY SELECTION: N-of-1 studies of pharmacological interventions with at least two subjects. DATA SYNTHESIS: Citation screening and data extractions were performed in duplicate. We performed statistical reanalysis testing for person-level HTE on all studies presenting person-level data. RESULTS: We identified 62 multiperson N-of-1 studies with at least two subjects. Statistical tests examining HTE were described in only 13 (21%), of which only two (3%) tested person-level HTE. Only 25 studies (40%) provided person-level data sufficient to reanalyse person-level HTE. Reanalysis using a fixed effect linear model identified statistically significant person-level HTE in 8 of the 13 studies (62%) reporting person-level treatment effects and in 8 of the 14 studies (57%) reporting person-level outcomes. CONCLUSIONS: Our analysis suggests that person-level HTE is common and often substantial. Reviewed studies had incomplete information on person-level treatment effects and their variation. Improved assessment and reporting of person-level treatment effects in multiperson N-of-1 studies are needed.


Assuntos
Ensaios Clínicos como Assunto , Estudos Cross-Over , Medicina Baseada em Evidências/métodos , Humanos , Modelos Estatísticos , Terapêutica/estatística & dados numéricos , Resultado do Tratamento
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