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Body Mass Index of 92,027 patients acutely admitted to general hospitals in Denmark: Associated clinical characteristics and 30-day mortality.

ABSTRACT: Data are sparse on the range of BMI among patients acutely admitted to general hospitals. We investigated BMI values and associated patient characteristics, reasons for hospital admission, and mortality in Denmark.We identified all persons with an acute inpatient admission 2011-2014 in Central Denmark Region and assessed BMI measurements recorded in the Clinical Information System. We used cross-sectional and cohort analyses to examine the BMI distribution and its association with demographic characteristics, comorbidities, medication use, tobacco smoking, reasons for admission, and 30-day mortality.Among 92,027 acutely admitted patients (median age 62 years, 49% female) with a BMI measurement, 4% had a BMI (kg/m2) <18.5, 42% a BMI between 18.5 and 25, 34% a BMI between 25 and 30, and 20% a BMI ≥30. Compared with normal-weight patients, 30-day mortality was high among patients with BMI <18.5 (7.5% vs. 2.8%, age- and smoking-adjusted odds ratio (aOR) 2.4; 95% confidence interval (CI): 2.0-2.9, whereas patients with overweight (aOR 0.7; 95% CI: 0.6-0.8) and obesity class I (aOR 0.8; 95% CI: 0.6-0.9)). Compared with the total population, patients with BMI <18.5 were older (68 years median); more were female (73%); more had comorbidities (Charlson Comorbidity Index score >0 in 42% vs. 33% overall), more were current smokers (45% vs. 27% overall), and acute admissions due to respiratory diseases or femoral fractures were frequent. In contrast, patients with BMI ≥30 were relatively young (59 years median), fewer smoked (24%): type 2 diabetes, sleep disorders, cholelithiasis, and heart failure were frequent diagnoses. Prevalence of therapies for metabolic syndrome, pain, and psychiatric disorders increased with higher BMI, while patients with BMI <18.5 frequently used asthma medications, glucocorticoids, and antibiotics.In patients acutely admitted to general hospitals, reasons for hospital admission and associated clinical characteristics differ substantially according to BMI range. BMI <18.5 is a clinical predictor of high short-term mortality.

SUBMITTER: Gribsholt SB

PROVIDER: S-EPMC5901987 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:OBJECTIVE:This study aimed to explore the potential for the Waterlow score (WS) to be used as a predictor of 30-day mortality and length of hospital stay (LHS) in acutely admitted medical patients aged 65 years and older. DESIGN:Prospective observational cohort study. SETTING:UK District General Hospital. SUBJECTS:834 consecutive patients aged 65 years and older admitted acutely to medical specialties between 30 May and 22 July 2014. METHODS:Admission WS (range 4-64) assessment paired with the patient's status at 30?days in terms of mortality and their LHS. PRIMARY OUTCOMES:30-day mortality and length of inpatient stay. RESULTS:834 consecutive acute medical admissions had their WS recorded. 30-day mortality was 13.1% (109 deaths). A significant difference in the distribution of WS (p<0.001) was seen between those who survived (median 12) and those who died (median 16) within 30 days, particularly within respiratory (p<0.001), stroke (p<0.001), cardiology (p<0.016), non-respiratory infections (p<0.018) and trauma (p<0.044) subgroups. Odds of dying within 30 days increased threefold for every 10-unit increase in the WS (p<0.001, 95%?CI 2.1 to 4.3). LHS was also positively linearly associated with the WS in those who survived 30 days (median=5, IQR=10; r=0.32, p<0.01). A five-unit increase in WS was associated with approximately 5 days increase in LHS. On the other hand, quadratic regression showed this relationship was curvilinear and negative (concave) for those who died within 30 days where a five-unit increase in WS was associated with an approximately 10 days decrease in LHS. CONCLUSION:This study demonstrates an association between a high WS and both 30-day mortality and LHS. This is particularly significant for mortality in patients in the respiratory, stroke and cardiac subcategories. The WS, a nursing-led screening tool that is carried out on virtually all admissions to UK hospitals, could have additional use at the time of patient admission as a risk assessment tool for 30-day mortality as well as a predictor of LHS.

Project description:BackgroundGiven the increased attention to sepsis at the population level there is a need to assess hospital performance in the care of sepsis patients using widely-available administrative data. The goal of this study was to develop an administrative risk-adjustment model suitable for profiling hospitals on their 30-day mortality rates for patients with sepsis.MethodsWe conducted a retrospective cohort study using hospital discharge data from general acute care hospitals in Pennsylvania in 2012 and 2013. We identified adult patients with sepsis as determined by validated diagnosis and procedure codes. We developed an administrative risk-adjustment model in 2012 data. We then validated this model in two ways: by examining the stability of performance assessments over time between 2012 and 2013, and by examining the stability of performance assessments in 2012 after the addition of laboratory variables measured on day one of hospital admission.ResultsIn 2012 there were 115,213 sepsis encounters in 152 hospitals. The overall unadjusted mortality rate was 18.5%. The final risk-adjustment model had good discrimination (C-statistic = 0.78) and calibration (slope and intercept of the calibration curve = 0.960 and 0.007, respectively). Based on this model, hospital-specific risk-standardized mortality rates ranged from 12.2 to 24.5%. Comparing performance assessments between years, correlation in risk-adjusted mortality rates was good (Pearson's correlation = 0.53) and only 19.7% of hospitals changed by more than one quintile in performance rankings. Comparing performance assessments after the addition of laboratory variables, correlation in risk-adjusted mortality rates was excellent (Pearson's correlation = 0.93) and only 2.6% of hospitals changed by more than one quintile in performance rankings.ConclusionsA novel claims-based risk-adjustment model demonstrated wide variation in risk-standardized 30-day sepsis mortality rates across hospitals. Individual hospitals' performance rankings were stable across years and after the addition of laboratory data. This model provides a robust way to rank hospitals on sepsis mortality while adjusting for patient risk.

Project description:IntroductionNew financial incentives, such as reduced Medicare reimbursements, have led hospitals to closely monitor their readmission rates and initiate efforts aimed at reducing them. In this context, many surgical departments participate in the American College of Surgeons National Surgical Quality Improvement Program (NSQIP), which collects detailed demographic, laboratory, clinical, procedure and perioperative occurrence data. The availability of such data enables the development of data science methods which predict readmissions and, as done in this paper, offer specific recommendations aimed at preventing readmissions.Materials and methodsThis study leverages NSQIP data for 722,101 surgeries to develop predictive and prescriptive models, predicting readmissions and offering real-time, personalized treatment recommendations for surgical patients during their hospital stay, aimed at reducing the risk of a 30-day readmission. We applied a variety of classification methods to predict 30-day readmissions and developed two prescriptive methods to recommend pre-operative blood transfusions to increase the patient's hematocrit with the objective of preventing readmissions. The effect of these interventions was evaluated using several predictive models.ResultsPredictions of 30-day readmissions based on the entire collection of NSQIP variables achieve an out-of-sample accuracy of 87% (Area Under the Curve-AUC). Predictions based only on pre-operative variables have an accuracy of 74% AUC, out-of-sample. Personalized interventions, in the form of pre-operative blood transfusions identified by the prescriptive methods, reduce readmissions by 12%, on average, for patients considered as candidates for pre-operative transfusion (pre-operative hematoctic <30). The prediction accuracy of the proposed models exceeds results in the literature.ConclusionsThis study is among the first to develop a methodology for making specific, data-driven, personalized treatment recommendations to reduce the 30-day readmission rate. The reported predicted reduction in readmissions can lead to more than $20 million in savings in the U.S. annually.

Project description:ImportanceFrail patients are known to have poor perioperative outcomes. There is a paucity of literature investigating how the Modified Frailty Index (mFI), a validated measure of frailty, is associated with unplanned readmission among military veterans following surgery.ObjectiveTo understand the association between frailty and 30-day postoperative unplanned readmission.Design, setting, and participantsA retrospective cohort study was conducted among adult patients who underwent surgery and were discharged alive from Veterans Affairs hospitals for orthopedic, general, and vascular conditions between October 1, 2007, and September 30, 2014, with a postoperative length of stay between 2 and 30 days.ExposureFrailty, as calculated by the 11 variables on the mFI.Main outcomes and measuresThe primary outcome of interest is 30-day unplanned readmission. Secondary outcomes included any 30-day predischarge or postdischarge complication, 30-day postdischarge mortality, and 30-day emergency department visit.ResultsThe study sample included 236 957 surgical procedures (among 223 877 men and 13 080 women; mean [SD] age, 64.0 [11.3] years) from high-volume surgical specialties: 101 348 procedures (42.8%) in orthopedic surgery, 92 808 procedures (39.2%) in general surgery, and 42 801 procedures (18.1%) in vascular surgery. The mFI was associated with readmission (odds ratio [OR], 1.11; 95% CI, 1.10-1.12; R2 = 10.3%; C statistic, 0.71). Unadjusted rates of overall 30-day readmission (26 262 [11.1%]), postdischarge emergency department visit (34 204 [14.4%]), any predischarge (13 855 [5.9%]) or postdischarge (14 836 [6.3%]) complication, and postdischarge mortality (1985 [0.8%]) varied by frailty in a dose-dependent fashion. In analysis by individual mFI components using Harrell ranking, impaired functional status, identified as nonindependent functional status (OR, 1.16; 95% CI, 1.11-1.21; P < .01) or having a residual deficit from a prior cerebrovascular accident (OR, 1.17; 95% CI, 1.11-1.22; P < .01), contributed most to the ability of the mFI to anticipate readmission compared with the other components. Acutely impaired sensorium (OR, 1.12; 95% CI, 0.99-1.27; P = .08) and history of a myocardial infarction within 6 months (OR, 0.93; 95% CI, 0.81-1.06; P = .28) were not significantly associated with readmission.Conclusions and relevanceThe mFI is associated with poor surgical outcomes, including readmission, primarily due to impaired functional status. Targeting potentially modifiable aspects of frailty preoperatively, such as improving functional status, may improve perioperative outcomes and decrease readmissions.

Project description:AbstractHealth information technology (IT) is often proposed as a solution to fragmentation of care, and has been hypothesized to reduce readmission risk through better information flow. However, there are numerous distinct health IT capabilities, and it is unclear which, if any, are associated with lower readmission risk.To identify the specific health IT capabilities adopted by hospitals that are associated with hospital-level risk-standardized readmission rates (RSRRs) through path analyses using structural equation modeling.This STROBE-compliant retrospective cross-sectional study included non-federal U.S. acute care hospitals, based on their adoption of specific types of health IT capabilities self-reported in a 2013 American Hospital Association IT survey as independent variables. The outcome measure included the 2014 RSRRs reported on Hospital Compare website.A 54-indicator 7-factor structure of hospital health IT capabilities was identified by exploratory factor analysis, and corroborated by confirmatory factor analysis. Subsequent path analysis using Structural equation modeling revealed that a one-point increase in the hospital adoption of patient engagement capability latent scores (median path coefficient ß = -0.086; 95% Confidence Interval, -0.162 to -0.008), including functionalities like direct access to the electronic health records, would generally lead to a decrease in RSRRs by 0.086%. However, computerized hospital discharge and information exchange capabilities with other inpatient and outpatient providers were not associated with readmission rates.These findings suggest that improving patient access to and use of their electronic health records may be helpful in improving hospital performance on readmission; however, computerized hospital discharge and information exchange among clinicians did not seem as beneficial - perhaps because of the quality or timeliness of information transmitted. Future research should use more recent data to study, not just adoption of health IT capabilities, but also whether their usage is associated with lower readmission risk. Understanding which capabilities impact readmission risk can help policymakers and clinical stakeholders better focus their scarce resources as they invest in health IT to improve care delivery.

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Body Mass Index of 92,027 patients acutely admitted to general hospitals in Denmark: Associated clinical characteristics and 30-day mortality.

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