ライフサイエンスコーパス: risk adjustment

1 e metric, with concerns regarding inadequate risk adjustment.

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

3 similar relative use of these services after risk adjustment.

4 eadmission among hospitals after appropriate risk adjustment.

5 ceed cautiously and must include appropriate risk adjustment.

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

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

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

9 erious underpayment problems remaining after risk adjustment.

10 istry of Acute Cardiac Events covariates for risk adjustment.

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

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

13 etailed clinical data for case detection and risk adjustment.

14 Case mix index for risk adjustment.

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

16 ons of new treatments are inadequate without risk adjustment.

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

18 sepsis rates, compare outcomes, and perform risk adjustment.

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

20 between ICUs indicates the need for further risk adjustment.

21 prediction using health status measures for risk adjustment.

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

23 groups with no significant differences after risk adjustment.

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

25 sk of cardiovascular events disappears after risk adjustment.

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

27 sted cost (+$978/quartile, P < 0.0001) after risk adjustment.

28 1-year mortality, which was confirmed after risk adjustment.

29 azard ratios did not change materially after risk adjustments.

30 te infection prevention bundles and outcomes risk adjustments.

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

32 After risk adjustment, 90-day episode spending was $11,447 at

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

34 After risk adjustment, ACGF was slightly higher among recipien

35 line in all-cause mortality dissipated after risk adjustment (adjusted hazard ratio, 0.98 [95% CI, 0.

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

37 After multivariable risk adjustment, admission chloride levels remained inde

38 hildren between 1 month and 16 years (median Risk adjustment after congenital heart surgery Model for

39 is (HR, 1.14; 95% CI, 0.61 to 2.11) or after risk adjustment (aHR, 1.01; 95% CI, 0.5 to 2.2).

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

41 Risk adjustment allows a fairer comparison of SSI rates

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

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

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

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

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

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

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

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

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

51 After risk adjustment and propensity weighting, patients who h

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

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

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

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

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

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

58 he excess mortality persisted after multiple risk adjustments and sensitivity analyses.

59 data elements should be collected to improve risk adjustment, and developing new metrics that better

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

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

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

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

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

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

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

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

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

69 After risk adjustment by race, patients with diabetes showed a

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

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

72 Without risk adjustment, capitation rates are likely to overpay

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

74 Risk adjustment correlated with DOC in the hippocampi an

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

76 Risk adjustment did not eliminate completely these diffe

77 After risk adjustment, each additional minute of predicted gro

78 ss the effect of adding body mass index as a risk adjustment element to the Acute Physiology and Chro

79 Computerized risk adjustment employing routinely available data may f

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

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

82 Risk-adjustment equations used in these analyses must co

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

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

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

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

87 After risk adjustment, female sex was associated with a 48% in

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

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

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

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

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

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

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

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

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

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

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

99 After risk adjustment for markers of illness severity at time

100 Optimal risk adjustment for older hospitalized patients should i

101 After risk adjustment for patient and hospital-level factors,

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

103 Risk adjustment for SDH changed hospitals' penalty statu

104 ng IVSR-trained surgeons persisted following risk adjustment for severity of patient disease and indi

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

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

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

108 ent case-finding methods and applied limited risk-adjustment for potential confounders.

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

110 After risk adjustment, freedom from all-cause mortality favore

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

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

113 After risk adjustment, high-readiness EDs (persistent or chang

114 After risk adjustment, higher operator volume was associated w

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

116 After risk-adjustment, IJ cases had 20% (5%-33%) shorter fluor

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

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

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

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

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

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

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

124 This can be applied for risk adjustment in population-based stroke outcomes rese

125 Variables selected for risk adjustment in studies using administrative database

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

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

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

129 Risk adjustment included exclusion of patients with majo

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

131 Risk adjustment is an ACA provision requiring that a fed

132 Risk adjustment is an important component of quality ass

133 Risk adjustment is essential before comparing patient ou

134 Risk adjustment is essential in evaluating the performan

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

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

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

138 After risk adjustment, LOI was the strongest factor associated

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

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

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

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

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

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

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

146 Estimates were sensitive to the risk-adjustment method.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

164 Using the Hierarchical Condition Category risk adjustment model to illuminate influence of illness

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

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

167 The risk-adjustment model has excellent discrimination (area

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

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

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

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

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

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

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

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

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

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

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

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

180 Risk adjustment models for PTCA mortality have recently

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

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

183 B procedures even after employing multilevel risk adjustment models.

184 ase (CVD) prediction algorithms and clinical risk adjustment models.

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

186 Accurate risk-adjustment models are useful for clinical decision

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

188 Risk-adjustment models for percutaneous coronary interve

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

190 Conclusions Including early DNR status in risk-adjustment models significantly improved model fit

191 e impact on discrimination or calibration of risk-adjustment models.

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

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

194 After risk-adjustment, multifocality (HR 4.53, 95%CI 1.34-15.2

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

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

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

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

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

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

201 Risk adjustment of patient selection and technique in AL

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

203 A central discussion with considerations for risk adjustment of PRO-PMs, individualized PAD care, and

204 Risk adjustment of survival data was done using Cox prop

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

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

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

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

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

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

211 lear attribution, inadequate definitions and risk adjustment, or discordance with recent evidence.

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

213 After risk adjustment, overall patient outcomes were similar.

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

215 After risk adjustment, overweight and obese patients with acut

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

217 After risk adjustment, patients treated by CABG compared with

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

219 After risk adjustment, patients with primary care exposure had

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

221 Risk adjustment performed similarly for health plan coho

222 Risk adjustment performed well for most plans.

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

224 After risk adjustment, prior use of antiplatelet agents remain

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

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

227 After risk adjustment, QRS prolongation was associated with in

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

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

230 After risk adjustment, racial disparities in survival persiste

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

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

233 Although risk adjustment remains a cornerstone for comparing outc

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

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

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

237 Risk adjustment should empirically analyze for case mix

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

239 ver half of OPOs facing decertification, and risk adjustment suggests that underlying characteristics

240 After risk adjustment, team familiarity was not significantly

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

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

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

244 After risk adjustment, the odds of all-cause readmission were

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

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

247 After risk adjustment, the presence of DGF was not associated

248 After risk adjustment, the relative risk (RR) for AEs among pa

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

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

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

252 After risk adjustment, there was no significant association be

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

254 in HL (11.0% versus 8.0%; P=0.32), and after risk adjustment, there were no differences (AA women: ha

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

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

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

258 tially afflicted ill patients but even after risk-adjustment, they were twice as likely to die as com

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

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

261 Reliable outcome assessments need risk adjustment to allow comparisons.

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

263 r socioeconomic distress, improves ACS NSQIP risk-adjustment to predict outcomes and hospital cost.

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

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

266 After risk adjustment, transfusion and sepsis were associated

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

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

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

270 We performed the risk adjustment using logistic regression model.

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

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

273 Overall, adding body mass index as a risk adjustment variable led only to a minor improvement

274 as to why inclusion of race and ethnicity as risk adjustment variables in an OPO performance metric i

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

276 After risk adjustment, vertical SNF integration was associated

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

278 Risk adjustment was limited to that available in claims

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

280 Risk adjustment was performed by using administrative da

281 Risk adjustment was performed using a study-specific ris

282 Risk adjustment was performed using the logistic EuroSCO

283 A negative binomial regression model with risk adjustment was used to determine the association be

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

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

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

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

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

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

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

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

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

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

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

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

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

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

298 Risk-adjustment with multivariate regression demonstrate

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

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