To determine how the quality of life (QOL) of intensive care unit (ICU) survivors compares with the general population, changes over time, and is predicted by baseline characteristics.
Systematic ...literature review including MEDLINE, EMBASE, CINAHL and Cochrane Library. Eligible studies measured QOL > or = 30 days after ICU discharge using the Medical Outcomes Study 36-item Short Form (SF-36), EuroQol-5D, Sickness Impact Profile, or Nottingham Health Profile in representative populations of adult ICU survivors. Disease-specific studies were excluded.
Of 8,894 citations identified, 21 independent studies with 7,320 patients were reviewed. Three of three studies found that ICU survivors had significantly lower QOL prior to admission than did a matched general population. During post-discharge follow-up, ICU survivors had significantly lower QOL scores than the general population in each SF-36 domain (except bodily pain) in at least four of seven studies. Over 1-12 months of follow-up, at least two of four studies found clinically meaningful improvement in each SF-36 domain except mental health and general health perceptions. A majority of studies found that age and severity of illness predicted physical functioning.
Compared with the general population, ICU survivors report lower QOL prior to ICU admission. After hospital discharge, QOL in ICU survivors improves but remains lower than general population levels. Age and severity of illness are predictors of physical functioning. This systematic review provides a general understanding of QOL following critical illness and can serve as a standard of comparison for QOL studies in specific ICU subpopulations.
To summarize long-term quality of life (QOL) and the degree of variation in QOL estimates across studies of acute respiratory distress (ARDS) survivors.
A systematic review of studies evaluating QOL ...in ARDS survivors was conducted. Medline, EMBASE, CINAHL, pre-CINAHL, and the Cochrane Library were searched, and reference lists from relevant articles were evaluated. Two authors independently selected studies reporting QOL in adult survivors of ARDS or acute lung injury at least 30 days after intensive care unit discharge and extracted data on study design, patient characteristics, methods, and results.
Thirteen independent observational studies (557 patients) met inclusion criteria. Eight of these studies used eight different QOL instruments, allowing only qualitative synthesis of results. The five remaining studies (330 patients) measured QOL using the Medical Outcomes Study 36-Item Short Form survey (SF-36). Mean QOL scores were similar across these studies, falling within a range of 20 points for all domains. Pooled domain-specific QOL scores in ARDS survivors 6 months or later after discharge ranged from 45 (role physical) to 66 (social functioning), or 15-26 points lower than population norms, in all domains except mental health (11 points) and role physical (39 points). Corresponding confidence intervals were no wider than +/-9 points. Six studies all found stable or improved QOL over time, but only one found significant improvement beyond 6 months after discharge.
ARDS survivors in different clinical settings experience similar decrements in QOL. The precise magnitude of these decrements helps clarify the long-term prognosis for ARDS survivors.
Administrative claims data offer a rich data source for clinical research. However, its application to the study of diabetic lower extremity ulceration is lacking. Our objective was to create a ...widely applicable framework by which investigators might derive and refine the International Classification of Diseases, 9th and 10th revision (ICD-9 and ICD-10, respectively) codes for use in identifying diabetic, lower extremity ulceration.
We created a seven-step process to derive and refine the ICD-9 and ICD-10 coding lists to identify diabetic lower extremity ulcers. This process begins by defining the research question and the initial identification of a list of ICD-9 and ICD-10 codes to define the exposures or outcomes of interest. These codes are then applied to claims data, and the rates of clinical events are examined for consistency with prior research and changes across the ICD-9 to ICD-10 transition. The ICD-9 and ICD-10 codes are then cross referenced with each other to further refine the lists.
Using this method, we started with 8 ICD-9 and 43 ICD-10 codes used to identify lower extremity ulcers in patients with known diabetes and peripheral arterial disease and examined the association of ulceration with lower extremity amputation. After refinement, we had 45 ICD-9 codes and 304 ICD-10 codes. We then grouped the codes into eight clinical exposure groups and examined the rates of amputation as a rudimentary test of validity. We found that the rate of lower extremity amputation correlated with the severity of lower extremity ulceration.
We identified 45 ICD-9 and 304 ICD-10 ulcer codes, which identified patients at risk of amputation from diabetes and peripheral artery disease. Although further validation at the medical record level is required, these codes can be used for claims-based risk stratification for long-term outcomes assessment in the treatment of patients at risk of limb loss.
: Background: Valganciclovir prophylaxis is reportedly associated with a low incidence of ganciclovir‐resistant cytomegalovirus (CMV). We assessed the incidence, clinical features, and outcome of ...drug‐resistant CMV among solid organ transplant patients who received valganciclovir prophylaxis.
Methods: The medical records of all CMV D+/R− kidney, pancreas, liver, and heart recipients were screened for CMV disease, and the clinical course and outcomes of patients with drug‐resistant CMV were reviewed.
Results: During a four‐yr‐study period, a total of 225 CMV D+/R− transplant patients received valganciclovir prophylaxis for a median of 92 d. Sixty‐five (29%) of the 225 patients developed delayed‐onset primary CMV disease, including nine (14%) suspected to have drug‐resistant virus. Four (6.2%) had confirmed UL97 or UL54 mutations. All except one patient manifested gastrointestinal tissue‐invasive disease. Together with reduction in immunosuppression, intravenous foscarnet with or without CMV hyperimmunoglobulin was the most common treatment. Drug‐associated nephrotoxicity was commonly observed and resulted in allograft loss in two patients. During the mean follow‐up of 2.2 yr, allograft loss and mortality occurred in two of four patients with proven and in three of five patients with clinically suspected drug‐resistant CMV.
Conclusions: Cytomegalovirus disease because of clinically suspected or genotypically confirmed drug‐resistant CMV is not uncommon in CMV D+/R− solid organ transplant patients who received valganciclovir prophylaxis. Because of its significant morbidity and mortality, an optimized strategy of CMV prevention is warranted to reduce the negative impact of drug‐resistant CMV on the successful outcome of organ transplantation.
Peripheral artery disease (PAD) stems from atherosclerosis of lower extremity arteries with resultant arterial narrowing or occlusion. The most severe form of PAD is termed chronic limb-threatening ...ischemia and carries a significant risk of limb loss and cardiovascular mortality. Diabetes mellitus is known to increase the incidence of PAD, accelerate disease progression, and increase disease severity. Patients with concomitant diabetes mellitus and PAD are at high risk for major complications, such as amputation. Despite a decrease in the overall number of amputations performed annually in the United States, amputation rates among those with both diabetes mellitus and PAD have remained stable or even increased in high-risk subgroups. Within this cohort, there is significant regional, racial/ethnic, and socioeconomic variation in amputation risk. Specifically, residents of rural areas, African-American and Native American patients, and those of low socioeconomic status carry the highest risk of amputation. The burden of amputation is severe, with 5-year mortality rates exceeding those of many malignancies. Furthermore, caring for patients with PAD and diabetes mellitus imposes a significant cost to the healthcare system-estimated to range from $84 billion to $380 billion annually. Efforts to improve the quality of care for those with PAD and diabetes mellitus must focus on the subgroups at high risk for amputation and the disparities they face in the receipt of both preventive and interventional cardiovascular care. Better understanding of these social, economic, and structural barriers will prove to be crucial for cardiovascular physicians striving to better care for patients facing this challenging combination of chronic diseases.
This study illustrates the use of logistic regression and machine learning methods, specifically random forest models, in health services research by analyzing outcomes for a cohort of patients with ...concomitant peripheral artery disease and diabetes mellitus.
Cohort study using fee-for-service Medicare beneficiaries in 2015 who were newly diagnosed with peripheral artery disease and diabetes mellitus. Exposure variables include whether patients received preventive measures in the 6 months following their index date: HbA1c test, foot exam, or vascular imaging study. Outcomes include any reintervention, lower extremity amputation, and death. We fit both logistic regression models as well as random forest models.
There were 88,898 fee-for-service Medicare beneficiaries diagnosed with peripheral artery disease and diabetes mellitus in our cohort. The rate of preventative treatments in the first six months following diagnosis were 52% (n = 45,971) with foot exams, 43% (n = 38,393) had vascular imaging, and 50% (n = 44,181) had an HbA1c test. The directionality of the influence for all covariates considered matched those results found with the random forest and logistic regression models. The most predictive covariate in each approach differs as determined by the t-statistics from logistic regression and variable importance (VI) in the random forest model. For amputation we see age 85 + (t = 53.17) urban-residing (VI = 83.42), and for death (t = 65.84, VI = 88.76) and reintervention (t = 34.40, VI = 81.22) both models indicate age is most predictive.
The use of random forest models to analyze data and provide predictions for patients holds great potential in identifying modifiable patient-level and health-system factors and cohorts for increased surveillance and intervention to improve outcomes for patients. Random forests are incredibly high performing models with difficult interpretation most ideally suited for times when accurate prediction is most desirable and can be used in tandem with more common approaches to provide a more thorough analysis of observational data.
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