ライフサイエンスコーパス: risk adjustment

1 e the utility of IMRS as a tool for clinical risk adjustment.

2 models of all-cause mortality were used for risk adjustment.

3 erious underpayment problems remaining after risk adjustment.

4 istry of Acute Cardiac Events covariates for risk adjustment.

5 MITATION: Medicare claims data were used for risk adjustment.

6 s, and comorbid conditions; and a measure of risk adjustment.

7 Case mix index for risk adjustment.

8 groups with no significant differences after risk adjustment.

9 ent bias', some of which can be corrected by risk adjustment.

10 ons of new treatments are inadequate without risk adjustment.

11 the influence of case mix on the process of risk adjustment.

12 ur index may be useful for clinical care and risk adjustment.

13 y intensivist, or absence of residents after risk adjustment.

14 between ICUs indicates the need for further risk adjustment.

15 prediction using health status measures for risk adjustment.

16 sk of cardiovascular events disappears after risk adjustment.

17 d in this high-performing category following risk adjustment.

18 hospitals' DTN and D2B times persisted after risk adjustment.

19 1-year mortality, which was confirmed after risk adjustment.

20 a hospital" for purposes of benchmarking and risk adjustment.

21 similar relative use of these services after risk adjustment.

22 eadmission among hospitals after appropriate risk adjustment.

23 ceed cautiously and must include appropriate risk adjustment.

24 sis mortality model (c-statistic, 0.826) for risk adjustment.

25 azard ratios did not change materially after risk adjustments.

26 mortality (cited by 78 percent), inadequate risk adjustment (79 percent), and the unreliability of d

27 After risk adjustment, a PR>/=230 ms (versus PR<230 ms) was as

28 ts and decreased patient survival even after risk adjustment (adjusted hazard ratio=1.33, 95% confide

29 After multivariable risk adjustment, admission chloride levels remained inde

30 Vendors applied their risk-adjustment algorithms and provided predicted probab

31 Risk adjustment allows a fairer comparison of SSI rates

32 ion (only available in cancer registries) in risk adjustment altered measured hospital performance.

33 with extended follow-up revealed that after risk adjustment, an interaction between early treatment

34 et was used to overcome limitations of prior risk-adjustment analyses.

35 rative mortality, examined using both direct risk adjustment and a matched-pairs analysis based on pr

36 les (n = 4860) to develop several models for risk adjustment and applied them to 38 providers perform

37 ital outcomes for older persons, but current risk adjustment and burden of illness assessment indices

38 A hybrid approach using claims data for risk adjustment and clinical data for complications may

39 discuss how to interpret estimates from the risk adjustment and IV methods when the treatment effect

40 e found to have broadly intact processing of risk adjustment and probability judgement, and to bet si

41 A propensity score risk adjustment and propensity-based matching analysis w

42 e regulatory provisions in the ACA requiring risk adjustment and reinsurance can help protect health

43 ms in the Affordable Care Act (ACA), namely, risk adjustment and reinsurance, might perform to ensure

44 hospital mortality is a valid instrument for risk adjustment and risk stratification in contemporary

45 Profiling accuracy is improved by risk adjustment and shrinkage adjustment to stabilize es

46 r, these results have been questioned as the risk adjustment and VTE measurement relied on administra

47 Standards for appropriate modeling, risk adjustment, and evaluation ("scorecarding") in this

48 in impulsivity, risk taking, deliberation or risk adjustment, and how this relates to brain pathology

49 These models were used for risk adjustment, and the relations between both yearly c

50 is measured, the ideal denominator, need for risk adjustment, and whether data are available.

51 After risk adjustment, anemia at discharge, but not admission,

52 After clinical risk adjustment, any BARC bleeding was independently ass

53 After risk adjustment, Asian American patients with AIS had lo

54 actice guidelines, the need for consensus on risk adjustment, better understanding of volume-outcome

55 After risk-adjustment, BMI was independently associated with h

56 spital volume-outcome studies that performed risk adjustment by using clinical data were less likely

57 equences for children and adolescents or how risk adjustment can augment pediatric performance incent

58 Without risk adjustment, capitation rates are likely to overpay

59 Before and after risk adjustment, cardiac death risk increased significan

60 Risk adjustment correlated with DOC in the hippocampi an

61 After risk adjustment, CRT-D use was associated with a reducti

62 Risk adjustment did not eliminate completely these diffe

63 Computerized risk adjustment employing routinely available data may f

64 admission resulted in substantially improved risk-adjustment equations (mean [SD] c statistic of 0.84

65 DESIGN, SETTING, AND PATIENTS: Comparison of risk-adjustment equations for inpatient mortality from J

66 Risk-adjustment equations used in these analyses must co

67 dings adds little to the predictive power of risk-adjustment equations.

68 After multivariable risk adjustment, era 3 had significantly decreased 2- an

69 After risk adjustment, every 10% increase in composite adheren

70 After risk adjustment, excess body weight was not associated w

71 l and score is a tool that provides reliable risk adjustment for administrative data.

72 After risk adjustment for age, gender, CAD, cholesterol, diabe

73 or both bedside clinical decision making and risk adjustment for assessment of quality.

74 answer two questions: (1) does comprehensive risk adjustment for comorbid illness and frailty measure

75 ntion may assist in patient selection and in risk adjustment for comparison of outcomes between provi

76 The risk adjustment for congenital heart surgery (RACHS-1) m

77 Patients were stratified using the Risk Adjustment for Congenital Heart Surgery algorithm.

78 rval, 0.47-0.76; P<0.05) after adjusting for Risk Adjustment for Congenital Heart Surgery risk catego

79 aradigm shift from the current postoperative risk adjustment for cross-hospital comparison to patient

80 Comparison of outcomes requires adequate risk adjustment for differences in patient risk and the

81 e among intensive care units (ICUs) requires risk adjustment for differences in severity of illness a

82 After risk adjustment for markers of illness severity at time

83 Optimal risk adjustment for older hospitalized patients should i

84 omes after pancreatoduodenectomy; therefore, risk adjustment for performance assessment and comparati

85 Risk adjustment for SDH changed hospitals' penalty statu

86 e consistently associated with increased CVD risk, adjustment for other risk factors (especially high

87 A valid and simple method of risk-adjustment for neonatal intensive care is important

88 assessment of mitral regurgitation, despite risk-adjustment for patient variables, likely because of

89 identify high-risk patient groups and inform risk-adjustment for standardized readmission rates.

90 ween these 2 eras remained significant after risk adjustment (hazard ratio, 0.82; 95% confidence inte

91 /Expected" (O/E) ratios between periods with risk adjustment held constant.

92 After risk adjustment, hospital factors explained 36% and 54%

93 cal risk scores reported, and strategies for risk adjustment in addition to reported mortality rates.

94 To longitudinally assess whether risk adjustment in Associating Liver Partition and Porta

95 maturity, genetic syndrome, type of surgery (Risk Adjustment in Congenital Heart Surgery [RACHS-1] ca

96 ve attempted to measure case complexity: the Risk Adjustment in Congenital Heart Surgery-1 and the Ar

97 This validated method provides a means of risk adjustment in groups of newborns undergoing noncard

98 the use of the Acute Organ Failure Score for risk adjustment in ICU research and outcomes reporting u

99 ortality was independently associated with a risk adjustment in patient selection (P < 0.001; OR: 1.6

100 Variables selected for risk adjustment in studies using administrative database

101 shows good potential for providing automated risk adjustment in the intensive care unit.

102 d to replace previously published models for risk adjustment in the UK.

103 by low-volume surgeons, but the adequacy of risk adjustment in those studies is in doubt.

104 Risk adjustment included exclusion of patients with majo

105 ical risk index for babies (CRIB) score is a risk-adjustment instrument widely used in neonatal inten

106 Risk adjustment is an ACA provision requiring that a fed

107 Risk adjustment is an important component of quality ass

108 Risk adjustment is essential before comparing patient ou

109 Risk adjustment is essential in evaluating the performan

110 Performance-measure risk adjustment is of great interest to hospital stakeho

111 rds need to be aware that, even when perfect risk adjustment is possible, the accuracy of hospital re

112 After risk adjustment, LGB was associated with a shorter lengt

113 After risk adjustment, LOI was the strongest factor associated

114 This type of risk adjustment may be adequate for evaluating hospital

115 Although methods of risk adjustment may be helpful in identifying patients f

116 use of clinical or claims-based diagnoses in risk adjustment may introduce important biases in compar

117 imate 30 day in-hospital mortality by use of risk adjustment measures including age, sex, admission t

118 ho: 0.88), individual rankings shifted after risk-adjustment (median Delta rank order: +/- 91.5; inte

119 istrative data are available, we recommend a risk-adjustment method based on diagnostic information.

120 ed surgical outcomes vary depending on which risk-adjustment method is applied.

121 Estimates were sensitive to the risk-adjustment method.

122 Third, the risk-adjustment methodology should include and accuratel

123 Sensitivity analyses based on alternative risk adjustment methods confirmed a pattern of increased

124 able physiologic data, a need exists for ICU risk adjustment methods that can be applied to administr

125 pital mortality rate comparisons of improved risk adjustment methods using diagnoses reported as pres

126 t based on the local-area practice style and risk adjustment methods, including conventional multivar

127 eport cards around the country use different risk adjustment methods.

128 Whether different risk-adjustment methods agree on the identity of ICU qua

129 could be used when other intensive care unit risk-adjustment methods are unavailable.

130 ed mortality ratios obtained using the three risk-adjustment methods.

131 OMR, and ASA and case mix were not included, risk adjustment might not be essential because the relat

132 The risk adjustment model explains 37% of the variation in L

133 A risk adjustment model for in-hospital mortality after PC

134 We sought to develop and evaluate a risk adjustment model for in-hospital mortality followin

135 We developed a well-performing risk adjustment model for SSI using electronically avail

136 The final risk adjustment model included procedure-type risk categ

137 ultiorgan transplants are defined, then each risk adjustment model is developed following a prespecif

138 ictor (motor GCS) with missing data from the risk adjustment model resulted in the least amount of ag

139 The QI is based on a robust risk adjustment model with good internal and temporal va

140 its peers, was consistent regardless of the risk-adjustment model applied, supporting their use as a

141 The risk-adjustment model has excellent discrimination (area

142 A useful risk-adjustment model must balance parsimony and ease of

143 We aimed to determine whether a sepsis risk-adjustment model that uses only administrative data

144 no evidence that adding comorbidites to the risk-adjustment model used to benchmark hospital perform

145 as "performance outliers" depending on which risk-adjustment model was used and how outlier status wa

146 ury Severity Score (ICISS) is the best-known risk-adjustment model when injuries are recorded using I

147 Patient cohorts for the risk adjustment models are identified, and single-organ

148 lized with heart failure, but do not improve risk adjustment models based on patient characteristics

149 The SRTR currently maintains 43 risk adjustment models for assessing posttransplant pati

150 We sought to validate recently proposed risk adjustment models for in-hospital percutaneous tran

151 the weak predictive validity of some of the risk adjustment models for morbidity, it may also repres

152 vely poor predictive validity of some of the risk adjustment models for morbidity.

153 ers for Disease Control and Prevention (CDC) risk adjustment models for pay-for-performance SSI did n

154 Risk adjustment models for PTCA mortality have recently

155 he same hospitals by patient-level mortality risk adjustment models using present-at-admission diagno

156 regarding known covariate limitations to the risk adjustment models, statistical noise alone leads to

157 B procedures even after employing multilevel risk adjustment models.

158 e of the discriminatory power of alternative risk-adjustment models (administrative, present on admis

159 Accurate risk-adjustment models are useful for clinical decision

160 known whether accounting for SES can improve risk-adjustment models for 30-day outcomes among Centers

161 Risk-adjustment models for percutaneous coronary interve

162 units, adding complex chronic conditions to risk-adjustment models led to greater model accuracy but

163 Two risk-adjustment models: a baseline model adjusted for se

164 edical centers; development of multivariable risk-adjustment models; identification of high and low o

165 With preoperative risk-adjustment now well-developed, the role of intraope

166 After risk adjustment, NSAIDs were associated with a 24% incre

167 ociated with recent femoral proportion after risk-adjustment (odds ratio, 0.97; 95% confidence interv

168 Automation could broaden access to risk adjustment of ICU outcomes with only a small trade-

169 , price transparency of the insurance plans, risk adjustment of insurers, and solidarity.

170 t of clinical variables has been defined for risk adjustment of observed outcomes for baseline differ

171 Risk adjustment of patient selection and technique in AL

172 A survey indicated risk adjustment of patient selection in all centers and

173 Risk adjustment of survival data was done using Cox prop

174 differences were no longer significant after risk adjustment on 30-day (hazard ratio, 1.02; 95% confi

175 County-level SES did not improve risk adjustment or change hospital rankings for 30-day m

176 Few studies used clinical data for risk adjustment or examined effects of hospital and phys

177 rgical risk factor not present in Medicare's risk adjustment or payment algorithms, as BMI is not col

178 These findings were not affected by risk adjustment or use of alternative definitions of wee

179 r patient risk and center effects using both risk adjustment (OR, 0.94; 95% CI, 0.91-0.97) and treatm

180 e is a continuing need to improve methods of risk adjustment, our results provide a basis for hospita

181 Consequently, with risk adjustment, overall profit margin decreased from 5.

182 After risk adjustment, overweight and obese patients with acut

183 After risk adjustment, patients had lower rates of 3-month dep

184 Also, after risk adjustment, patients with insulin-dependent diabete

185 ing illustrates the fallacy of assuming that risk adjustment per se is sufficient to permit direct si

186 Risk adjustment performed similarly for health plan coho

187 Risk adjustment performed well for most plans.

188 ibute, it is considered a critical factor in risk-adjustment policies designed to reward efficient an

189 After risk adjustment, prior use of antiplatelet agents remain

190 s; however, it is unclear to what extent the risk-adjustment process itself may affect these metrics.

191 outcome of interest and (2) a comprehensive risk-adjustment process to control for differences in pa

192 After risk adjustment, QRS prolongation was associated with in

193 Instrumental variable (IV) and risk adjustment (RA) estimators, including propensity sc

194 After risk adjustment, race did not predict operative (odds ra

195 After risk adjustment, racial disparities in survival persiste

196 Thus, whereas risk adjustment reduced women's OM from 90% higher than

197 Current methods of risk adjustment rely on diagnoses recorded in clinical a

198 Although risk adjustment remains a cornerstone for comparing outc

199 After risk-adjustment, rural patients had lower rates of chole

200 We sought to create an automated ICU risk adjustment score, based on the Simplified Acute Phy

201 Comorbidity-based risk adjustment should be strongly considered by the CDC

202 Risk adjustment should empirically analyze for case mix

203 ent to all centers exploring center-specific risk adjustment strategies.

204 than 30% macrosteatotis should be used with risk adjustment, that is, up to BAR score of 9 or less.

205 Following risk adjustment, the difference in mortality rates was a

206 Before risk adjustment, the median hospital survival rate was 2

207 After risk adjustment, the oldest patients were 27 times more

208 After risk adjustment, the predicted event rates were nearly i

209 After risk-adjustment, the median length of stay remained 0.5

210 After risk-adjustment, the Surgical Apgar Score remained stron

211 After risk adjustment, there was no difference in 1-year morta

212 After risk adjustment, there was no significant association be

213 For the overall PCI cohort, after risk adjustment, there was no significant evidence of wo

214 After propensity score matching and risk-adjustment, there was no significant association of

215 s, 31% received a high-intensity dose; after risk adjustment, these patients had outcomes similar to

216 ables conceal good and bad runs, and without risk adjustment they are difficult to interpret.

217 tcomes across hospitals requires appropriate risk adjustment to account for differences in patient ca

218 sures and developing appropriate methods for risk adjustment to adequately control for patient select

219 Reliable outcome assessments need risk adjustment to allow comparisons.

220 After reliability and risk adjustment to the median patient, adjusted hospital

221 ugmented hybrid methods, a novel approach to risk adjustment, to adjust for LOS risk factors from the

222 ute renal failure remain extremely high, and risk-adjustment tools are needed for quality improvement

223 After risk adjustment, transfusion and sepsis were associated

224 An easy to interpret validated risk adjustment Tree model using blood test and NEWS tak

225 Three methodologies of risk adjustment (University Health Consortium, Physiolog

226 pitals that treat patients with cancer after risk adjustment using information in Medicare administra

227 We performed the risk adjustment using logistic regression model.

228 measured in trauma patients before and after risk adjustment using propensity scoring.

229 with all-cause mortality was assessed after risk-adjustment using Cox proportional hazards models.

230 We selected risk-adjustment variables by expert consultation and boo

231 clinically ascertained outcomes and detailed risk adjustment, VTE rates reflect hospital imaging use

232 Risk adjustment was limited to that available in claims

233 as still significant but attenuated when the risk adjustment was modified to adjust for mitral valve

234 Risk adjustment was performed by using administrative da

235 Risk adjustment was performed using a study-specific ris

236 Risk adjustment was performed using the logistic EuroSCO

237 rmance was known with certainty, and perfect risk-adjustment was feasible.

238 After risk adjustment, we found that traumatic brain injury pa

239 To determine the need for risk adjustment, we used univariate and multivariate log

240 Survival analyses with and without risk adjustment were performed from the time of warfarin

241 mportant in clinical decision making and for risk adjustment when assessing quality of care.

242 iscrimination and offers a novel approach to risk adjustment which may potentially support clinical d

243 ies in patient populations, methodology, and risk adjustment, which produced substantial variability

244 predicted risk and in clinical research for risk adjustment while comparing outcomes of different th

245 alth plan compensation under diagnosis-based risk adjustment with actual health care expenditures, un

246 d an intention-to-treat analysis, performing risk adjustment with adjustment for and matching to prop

247 gth of this public reporting effort included risk adjustment with clinical rather than administrative

248 ofrontal gyrus, insula and caudate; abnormal risk adjustment with increased apparent diffusion coeffi

249 lected from the preceding year were used for risk adjustment with logistic regression.

250 However, further risk adjustment with the addition of the highly signific

251 Secondary analyses including studies without risk adjustment, with a composite exposure of organizati

252 exposure definitions, outcome measures, and risk adjustment, with the greatest heterogeneity seen in