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Journal of the American Academy of... Aug 2018
Topics: Big Data; Dermatology; Electronic Data Processing; Humans; Quality Improvement; Registries; Reimbursement Mechanisms; Risk Adjustment; Societies, Medical
PubMed: 29574090
DOI: 10.1016/j.jaad.2018.03.020 -
Clinical Interventions in Aging 2018Pharmacologic management of infections in elderly patients presents multiple challenges to health care professionals due to variable pharmacokinetics, pharmacodynamics,... (Review)
Review
Pharmacologic management of infections in elderly patients presents multiple challenges to health care professionals due to variable pharmacokinetics, pharmacodynamics, and immune function. Age is a well-established risk factor for infection, but furthermore is a risk factor for prolonged length of hospital stay, increased incidence of complications, and significant and sustained decline in baseline functional status. In 2014, 46.2 million Americans were aged ≥65 years, accounting for 14.5% of the total population. By 2033, for the first time, the population of persons aged ≥65 years is projected to outnumber the people <18 years of age. According to the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey, 154 million prescriptions for antimicrobials were estimated to have been written in doctors' offices and emergency departments during a 1-year time period. In 2014, 266.1 million courses of antimicrobials were dispensed to outpatients by US community pharmacies. A study that evaluated 2007-2009 Medicare Part D data found that patients aged ≥65 years used more antimicrobials, at 1.10 per person per year, compared to 0.88 antimicrobials used per person per year in patients aged 0-64 years. With the abundance of antimicrobial prescriptions and the current growth in the number and proportion of older adults in the US, it is essential that health care providers understand appropriate antimicrobial pharmacotherapy in the elderly patient. This review focuses on the use and implications of antimicrobial agents in the elderly population.
Topics: Aged; Aged, 80 and over; Aging; Anti-Infective Agents; Drug-Related Side Effects and Adverse Reactions; Female; Humans; Male; Risk Adjustment; United States
PubMed: 29713150
DOI: 10.2147/CIA.S133640 -
Infection Control and Hospital... Aug 2021To determine whether electronically available comorbidities and laboratory values on admission are risk factors for hospital-onset Clostridioides difficile infection...
OBJECTIVE
To determine whether electronically available comorbidities and laboratory values on admission are risk factors for hospital-onset Clostridioides difficile infection (HO-CDI) across multiple institutions and whether they could be used to improve risk adjustment.
PATIENTS
All patients at least 18 years of age admitted to 3 hospitals in Maryland between January 1, 2016, and January 1, 2018.
METHODS
Comorbid conditions were assigned using the Elixhauser comorbidity index. Multivariable log-binomial regression was conducted for each hospital using significant covariates (P < .10) in a bivariate analysis. Standardized infection ratios (SIRs) were computed using current Centers for Disease Control and Prevention (CDC) risk adjustment methodology and with the addition of Elixhauser score and individual comorbidities.
RESULTS
At hospital 1, 314 of 48,057 patient admissions (0.65%) had a HO-CDI; 41 of 8,791 patient admissions (0.47%) at community hospital 2 had a HO-CDI; and 75 of 29,211 patient admissions (0.26%) at community hospital 3 had a HO-CDI. In multivariable regression, Elixhauser score was a significant risk factor for HO-CDI at all hospitals when controlling for age, antibiotic use, and antacid use. Abnormal leukocyte level at hospital admission was a significant risk factor at hospital 1 and hospital 2. When Elixhauser score was included in the risk adjustment model, it was statistically significant (P < .01). Compared with the current CDC SIR methodology, the SIR of hospital 1 decreased by 2%, whereas the SIRs of hospitals 2 and 3 increased by 2% and 6%, respectively, but the rankings did not change.
CONCLUSIONS
Electronically available patient comorbidities are important risk factors for HO-CDI and may improve risk-adjustment methodology.
Topics: Clostridioides; Clostridioides difficile; Clostridium Infections; Comorbidity; Cross Infection; Electronic Health Records; Hospitals; Humans; Risk Adjustment
PubMed: 33327970
DOI: 10.1017/ice.2020.1344 -
Value in Health : the Journal of the... Jun 2020Although comorbidities play an essential role in risk adjustment and outcomes measurement, there is little consensus regarding the best source of this data. The aim of...
OBJECTIVES
Although comorbidities play an essential role in risk adjustment and outcomes measurement, there is little consensus regarding the best source of this data. The aim of this study was to identify general patient-reported morbidity instruments and their measurement properties.
METHODS
A systematic review was conducted using multiple electronic databases (Embase, Medline, Cochrane Central, and Web of Science) from inception to March 2018. Articles focusing primarily on the development or subsequent validation of a patient-reported morbidity instrument were included. After including relevant articles, the measurement properties of each morbidity instrument were extracted by 2 investigators for narrative synthesis.
RESULTS
A total of 1005 articles were screened, of which 34 eligible articles were ultimately included. The most widely assessed instruments were the Self-Reported Charlson Comorbidity Index (n = 7), the Self-Administered Comorbidity Questionnaire (n = 3), and the Disease Burden Morbidity Assessment (n = 3). The most commonly included conditions were diabetes, hypertension, and myocardial infarction. Studies demonstrated substantial variability in item-level reliability versus the gold standard medical record review (κ range 0.66-0.86), meaning that the accuracy of the self-reported comorbidity data is dependent on the selected morbidity.
CONCLUSIONS
The Self-Reported Charlson Comorbidity Index and the Self-Administered Comorbidity Questionnaire were the most frequently cited instruments. Significant variability was observed in reliability per comorbid condition of patient-reported morbidity questionnaires. Further research is needed to determine whether patient-reported morbidity data should be used to bolster medical records data or serve as a stand-alone entity when risk adjusting observational outcomes data.
Topics: Comorbidity; Humans; Morbidity; Outcome Assessment, Health Care; Patient Reported Outcome Measures; Reproducibility of Results; Risk Adjustment; Surveys and Questionnaires
PubMed: 32540238
DOI: 10.1016/j.jval.2020.02.006 -
The American Journal of Managed Care Feb 2016Case-mix adjustment is generally considered indispensable for fair comparison of healthcare performance. Inaccurate results are also unfair to patients as they are... (Review)
Review
OBJECTIVES
Case-mix adjustment is generally considered indispensable for fair comparison of healthcare performance. Inaccurate results are also unfair to patients as they are ineffective for improving quality. However, little is known about what factors should be adjusted for. We reviewed case-mix factors included in adjustment models for key diabetes indicators, the rationale for their inclusion, and their impact on performance.
STUDY DESIGN
Systematic review.
METHODS
This systematic review included studies published up to June 2013 addressing case-mix factors for 6 key diabetes indicators: 2 outcomes and 2 process indicators for glycated hemoglobin (A1C), low-density lipoprotein cholesterol, and blood pressure. Factors were categorized as demographic, diabetes-related, comorbidity, generic health, geographic, or care-seeking, and were evaluated on the rationale for inclusion in the adjustment models, as well as their impact on indicator scores and ranking.
RESULTS
Thirteen studies were included, mainly addressing A1C value and measurement. Twenty-three different case-mix factors, mostly demographic and diabetes-related, were identified, and varied from 1 to 14 per adjustment model. Six studies provided selection motives for the inclusion of case-mix factors. Marital status and body mass index showed a significant impact on A1C value. For the other factors, either no or conflicting associations were reported, or too few studies (n ≤ 2) investigated this association.
CONCLUSIONS
Scientific knowledge about the relative importance of case-mix factors for diabetes indicators is emerging, especially for demographic and diabetes-related factors and indicators on A1C, but is still limited. Because arbitrary adjustment potentially results in inaccurate quality information, meaningful stratification that demonstrates inequity in care might be a better guide, as it can be a driver for quality improvement.
Topics: Age Factors; Body Mass Index; Cholesterol, LDL; Comorbidity; Diabetes Mellitus; Diagnosis-Related Groups; Glycated Hemoglobin; Health Status; Humans; Patient Acceptance of Health Care; Risk Adjustment; Sex Factors; Socioeconomic Factors
PubMed: 26881319
DOI: No ID Found -
American Family Physician Nov 2021Fetal growth restriction, previously called intrauterine growth restriction, is a condition in which a fetus does not achieve its full growth potential during pregnancy....
Fetal growth restriction, previously called intrauterine growth restriction, is a condition in which a fetus does not achieve its full growth potential during pregnancy. Early detection and management of fetal growth restriction are essential because it has significant clinical implications in childhood. It is diagnosed by estimated fetal weight or abdominal circumference below the 10th percentile on formal ultrasonography. Early-onset fetal growth restriction is diagnosed before 32 weeks' gestation and has a higher risk of adverse fetal outcomes. There are no evidence-based measures for preventing fetal growth restriction; however, aspirin used for the prevention of preeclampsia in high-risk pregnancies may reduce the likelihood of developing it. Timing of delivery for pregnancies affected by growth restriction must be adjusted based on the risks of premature birth and ongoing gestation, and it is best determined in consultation with maternal-fetal medicine specialists. Neonates affected by fetal growth restriction are at risk of feeding difficulties, glucose instability, temperature instability, and jaundice. As these children age, they are at risk of abnormal growth patterns, as well as later cardiac, metabolic, neurodevelopmental, reproductive, and psychiatric disorders.
Topics: Early Diagnosis; Female; Fetal Growth Retardation; Humans; Infant, Newborn; Infant, Newborn, Diseases; Infant, Small for Gestational Age; Perinatology; Pregnancy; Premature Birth; Prenatal Care; Prenatal Exposure Delayed Effects; Preventive Health Services; Risk Adjustment; Ultrasonography, Prenatal
PubMed: 34783495
DOI: No ID Found -
The Lancet. Child & Adolescent Health Apr 2020
Topics: Gambia; Health Resources; Humans; Infant Mortality; Infant, Newborn; Risk Adjustment; United Kingdom
PubMed: 32119842
DOI: 10.1016/S2352-4642(20)30039-0 -
BMC Pregnancy and Childbirth Nov 2017Maternal critical illness occurs in 1.2 to 4.7 of every 1000 live births in the United States and approximately 1 in 100 women who become critically ill will die.... (Review)
Review
BACKGROUND
Maternal critical illness occurs in 1.2 to 4.7 of every 1000 live births in the United States and approximately 1 in 100 women who become critically ill will die. Patient characteristics and comorbid conditions are commonly summarized as an index or score for the purpose of predicting the likelihood of dying; however, most such indices have arisen from non-pregnant patient populations. We sought to systematically review comorbidity indices used in health administrative datasets of pregnant women, in order to critically appraise their measurement properties and recommend optimal tools for clinicians and maternal health researchers.
METHODS
We conducted a systematic search of MEDLINE and EMBASE to identify studies published from 1946 and 1947, respectively, to May 2017 that describe predictive validity of comorbidity indices using health administrative datasets in the field of maternal health research. We applied a methodological PubMed search filter to identify all studies of measurement properties for each index.
RESULTS
Our initial search retrieved 8944 citations. The full text of 61 articles were identified and assessed for final eligibility. Finally, two eligible articles, describing three comorbidity indices appropriate for health administrative data remained: The Maternal comorbidity index, the Charlson comorbidity index and the Elixhauser Comorbidity Index. These studies of identified indices had a low risk of bias. The lack of an established consensus-building methodology in generating each index resulted in marginal sensibility for all indices. Only the Maternal Comorbidity Index was derived and validated specifically from a cohort of pregnant and postpartum women, using an administrative dataset, and had an associated c-statistic of 0.675 (95% Confidence Interval 0.647-0.666) in predicting mortality.
CONCLUSIONS
Only the Maternal Comorbidity Index directly evaluated measurement properties relevant to pregnant women in health administrative datasets; however, it has only modest predictive ability for mortality among development and validation studies. Further research to investigate the feasibility of applying this index in clinical research, and its reliability across a variety of health administrative datasets would be incrementally helpful. Evolution of this and other tools for risk prediction and risk adjustment in pregnant and post-partum patients is an important area for ongoing study.
Topics: Female; Humans; Maternal Health; Maternal Mortality; Pregnancy; Reproducibility of Results; Research; Risk Adjustment; United States; Vital Statistics
PubMed: 29132349
DOI: 10.1186/s12884-017-1558-3 -
Circulation. Cardiovascular Quality and... Oct 2021Health status outcomes are increasingly being promoted as measures of health care quality, given their importance to patients. In heart failure (HF), an American College...
BACKGROUND
Health status outcomes are increasingly being promoted as measures of health care quality, given their importance to patients. In heart failure (HF), an American College of Cardiology/American Heart Association Task Force proposed using the proportion of patients with preserved health status as a quality measure but not as a performance measure because risk adjustment methods were not available.
METHODS
We built risk adjustment models for alive with preserved health status and for preserved health status alone in a prospective registry of outpatients with HF with reduced ejection fraction across 146 US centers between December 2015 and October 2017. Preserved health status was defined as not having a ≥5-point decrease in the Kansas City Cardiomyopathy Questionnaire Overall Summary score at 1 year. Using only patient-level characteristics, hierarchical multivariable logistic regression models were developed for 1-year outcomes and validated using data from 1 to 2 years. We examined model calibration, discrimination, and variability in sites' unadjusted and adjusted rates.
RESULTS
Among 3932 participants (median age [interquartile range] 68 years [59-75], 29.7% female, 75.4% White), 2703 (68.7%) were alive with preserved health status, 902 (22.9%) were alive without preserved health status, and 327 (8.3%) had died by 1 year. The final risk adjustment model for alive with preserved health status included baseline Kansas City Cardiomyopathy Questionnaire Overall Summary, age, race, employment status, annual income, body mass index, depression, atrial fibrillation, renal function, number of hospitalizations in the past 1 year, and duration of HF (optimism-corrected C statistic=0.62 with excellent calibration). Similar results were observed when deaths were ignored. The risk standardized proportion of patients alive with preserved health status across the 146 sites ranged from 62% at the 10th percentile to 75% at the 90th percentile. Variability across sites was modest and changed minimally with risk adjustment.
CONCLUSIONS
Through leveraging data from a large, outpatient, observational registry, we identified key factors to risk adjust sites' proportions of patients with preserved health status. These data lay the foundation for building quality measures that quantify treatment outcomes from patients' perspectives.
Topics: Aged; Female; Health Status; Heart Failure; Humans; Male; Quality of Life; Registries; Risk Adjustment; Stroke Volume; United States
PubMed: 34615366
DOI: 10.1161/CIRCOUTCOMES.121.008072 -
Annals of Surgery Apr 2016To assess whether differences in readmission rates between safety-net hospitals (SNH) and non-SNHs are due to differences in hospital quality, and to compare the results...
OBJECTIVE
To assess whether differences in readmission rates between safety-net hospitals (SNH) and non-SNHs are due to differences in hospital quality, and to compare the results of hospital profiling with and without SES adjustment.
BACKGROUND
In response to concerns that quality measures unfairly penalizes SNH, NQF recently recommended that performance measures adjust for socioeconomic status (SES) when SES is a risk factor for poor patient outcomes.
METHODS
Multivariate regression was used to examine the association between SNH status and 30-day readmission after major surgery. The results of hospital profiling with and without SES adjustment were compared using the CMS Hospital Compare and the Hospital Readmissions Reduction Program (HRRP) methodologies.
RESULTS
Adjusting for patient risk and SES, patients admitted to SNHs were not more likely to be readmitted compared with patients in in non-SNHs (AOR 1.08; 95% CI:0.95-1.23; P = 0.23). The results of hospital profiling based on Hospital Compare were nearly identical with and without SES adjustment (ICC 0.99, κ 0.96). Using the HRRP threshold approach, 61% of SNHs were assigned to the penalty group versus 50% of non-SNHs. After adjusting for SES, 51% of SNHs were assigned to the penalty group.
CONCLUSIONS
Differences in surgery readmissions between SNHs and non-SNHs are due to differences in the patient case mix of low-SES patients, and not due to differences in quality. Adjusting readmission measures for SES leads to changes in hospital ranking using the HRRP threshold approach, but not using the CMS Hospital Compare methodology. CMS should consider either adjusting for the effects of SES when calculating readmission thresholds for HRRP, or replace it with the approach used in Hospital Compare.
Topics: Adult; Aged; Aged, 80 and over; Databases, Factual; Female; Humans; Male; Middle Aged; Multivariate Analysis; New York; Patient Readmission; Postoperative Complications; Regression Analysis; Risk Adjustment; Safety-net Providers; Social Class; Surgical Procedures, Operative
PubMed: 26655922
DOI: 10.1097/SLA.0000000000001363