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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:ImportanceAchieving a body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) of less than 30 is an important goal of bariatric surgery, given the increased risk for weight-related morbidity and mortality with a BMI above this threshold.ObjectiveTo identify predictors for achieving a BMI of less than 30 after bariatric surgery.Design, setting, and participantsThis retrospective study used data from the Michigan Bariatric Surgery Collaborative, a statewide quality improvement collaborative that uses a prospectively gathered clinical data registry. A total of 27 320 adults undergoing primary bariatric surgery between June 2006 and May 2015 at teaching and nonteaching hospitals in Michigan were included.ExposureBariatric surgery.Main outcomes and measuresLogistic regression was used to identify predictors for achieving a BMI of less than 30 at 1 year after surgery. Secondary outcome measures included 30-day postoperative complications and 1-year self-reported comorbidity remission.ResultsA total of 9713 patients (36%; mean [SD] age, 46.9 [11.3] years; 16.6% male) achieved a BMI of less than 30 at 1 year after bariatric surgery. A significant predictor for achieving this goal was a preoperative BMI of less than 40 (odds ratio [OR], 12.88; 95% CI, 11.71-14.16; P < .001). Patients who had a sleeve gastrectomy, gastric bypass, or duodenal switch were more likely to achieve a BMI of less than 30 compared with those who underwent adjustable gastric banding (OR, 8.37 [95% CI, 7.44-9.43]; OR, 21.43 [95% CI, 18.98-24.19]; and OR, 82.93 [95% CI, 59.78-115.03], respectively; P < .001). Only 8.5% of patients with a BMI greater than 50 achieved a BMI of less than 30 after bariatric surgery. Patients who achieved a BMI of less than 30 had significantly higher reported rates of medication discontinuation for hyperlipidemia (60.7% vs 43.2%, P < .001), diabetes (insulin: 67.7% vs 50.0%, P < .001; oral medications: 78.5% vs 64.3%, P < .001), and hypertension (54.7% vs 34.6%, P < .001), as well as a significantly higher rate of sleep apnea remission (72.5% vs 49.3%, P < .001) and higher satisfaction rate (92.8% vs 78.0%, P < .001) compared with patients who did not.Conclusions and relevancePatients with a preoperative BMI of less than 40 are more likely to achieve a BMI of less than 30 after bariatric surgery and are more likely to experience comorbidity remission. Policies and practice patterns that delay bariatric surgery until the BMI is 50 or greater can result in significantly inferior outcomes.

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:ImportanceBody mass index (BMI) is used to diagnose obesity in adolescents worldwide, despite evidence that weight does not scale with height squared in adolescents. To account for this, health care providers diagnose obesity using BMI percentiles for each age (BMI z scores), but this does not ensure that BMI is accurate in adolescents.ObjectiveTo compare the accuracy of BMI vs other body fat indices of the form body mass divided by heightn in estimating body fat levels in adolescents.Design, setting, and participantsCross-sectional data from the 1999 to 2006 US National Health and Nutrition Examination Survey were analyzed between September 2015 and December 2016.Main outcomes and measuresDual-energy x-ray absorptiometry and anthropometric data were used to determine changes in body fat levels, body proportions, and the scaling relationships among body mass, height, and percent body fat. To assess the merits of each adiposity index, 3 criteria were used: stability with age, accuracy in estimating percent body fat, and accuracy in classifying adolescents as overweight vs normal weight.ResultsParticipants included 2285 non-Hispanic white participants aged 8 to 29 years. Percent body fat varied with both age and height during adolescence, invalidating the standard weight-to-height regression as the way of finding the optimal body fat index. Because the correct regression model (percent body fat is proportional to mass divided by heightn) suggested that percent body fat scales to height with an exponent closer to 3, we therefore focused on the tri-ponderal mass index (TMI; mass divided by height cubed) as an alternative to BMI z scores. For ages 8 to 17 years, TMI yielded greater stability with age and estimated percent body fat better than BMI (R2?=?0.64 vs 0.38 in boys and R2?=?0.72 vs 0.66 in girls). Moreover, TMI misclassified adolescents as overweight vs normal weight less often than BMI z scores (TMI, 8.4%; 95% CI, 7.3%-9.5% vs BMI, 19.4%; 95% CI, 17.8%-20.0%; P?<?.001) and performed equally as well as updated BMI percentiles derived from the same data set (TMI, 8.4%; 95% CI, 7.3%-9.5% vs BMI, 8.0%; 95% CI, 6.9%-9.1%; P?=?.62).Conclusions and relevanceThe tri-ponderal mass index estimates body fat levels more accurately than BMI in non-Hispanic white adolescents aged 8 to 17 years. Moreover, TMI diagnoses adolescents as overweight more accurately than BMI z scores and equally as well as updated BMI percentiles but is much simpler to use than either because it does not involve complicated percentiles. Taken together, it is worth considering replacing BMI z scores with TMI to estimate body fat levels in adolescents.

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.

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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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