ライフサイエンスコーパス: poor outcome

1 overexpressed in PCa and is associated with poor outcome.

2 tics and time to ART initiation or composite poor outcome.

3 mine use was not an independent predictor of poor outcome.

4 d identified IFITM3 as a strong predictor of poor outcome.

5 ally addressed the tumor biology that drives poor outcome.

6 new mode of therapy for this disease with a poor outcome.

7 T, and lung involvement were associated with poor outcome.

8 scular therapy (EVT) is a known predictor of poor outcome.

9 pression has been associated with a clinical poor outcome.

10 t, patients with biliary tract cancer have a poor outcome.

11 s were enrolled; 87 had good outcome, 56 had poor outcome.

12 C), promotes tumour development and predicts poor outcome.

13 th coronary microvascular dysfunction have a poor outcome.

14 ents with myocardial dysfunction and predict poor outcome.

15 is a common malignancy that has a relatively poor outcome.

16 ncers (TNBCs) and it is associated with very poor outcome.

17 sively managed, without specialty care, with poor outcome.

18 ukemia (CLL) patients and is associated with poor outcome.

19 with S10, S13 and S17, were associated with poor outcome.

20 d elevated levels of HP1gamma in PCa predict poor outcome.

21 children and adults, and is associated with poor outcome.

22 esources despite a predicted and often known poor outcome.

23 early postoperative complications predict a poor outcome.

24 l subtype tumor cells is responsible for its poor outcome.

25 ysphagia (PSD) is common and associated with poor outcome.

26 h were associated with 5.47 higher odds of a poor outcome.

27 n incidence of 3.6% and were associated with poor outcome.

28 complicated clinical course and/or risk of a poor outcome.

29 MB) is associated with treatment failure and poor outcome.

30 alterations were observed in the group with poor outcome.

31 olume, and electrographic seizures predicted poor outcome.

32 suppression and have been associated with a poor outcome.

33 d expression of Col1 and was associated with poor outcome.

34 t biomarker changes would be associated with poor outcome.

35 h clipping or coiling for predicting DCI and poor outcome.

36 to prevent failure to rescue and eventually poor outcome.

37 treated clinically as a single disease with poor outcomes.

38 treatment options and historically have had poor outcomes.

39 ng in missed diagnoses, delayed therapy, and poor outcomes.

40 were associated with known risk factors for poor outcomes.

41 e arising in the mediastinum have distinctly poor outcomes.

42 ajor elective surgery and is associated with poor outcomes.

43 ts and were strong independent predictors of poor outcomes.

44 phic cardiomyopathy (HCM) is associated with poor outcomes.

45 rosis could identify those at higher risk of poor outcomes.

46 ith HFrEF, SBP <130 mm Hg is associated with poor outcomes.

47 cute GI graft versus host disease (GvHD) and poor outcomes.

48 ents in high-risk categories still face very poor outcomes.

49 d is associated with exercise limitation and poor outcomes.

50 rt failure (HF) patients and associates with poor outcomes.

51 h acquired alterations in the cell cycle and poor outcomes.

52 l-dominated host response is associated with poor outcomes.

53 es of biofilm formation were associated with poor outcomes.

54 for the progression of breast cancer toward poor outcomes.

55 argeted therapies for WT patient groups with poor outcomes.

56 pancreatitis is a highly morbid disease with poor outcomes.

57 the most aggressive malignant neoplasms with poor outcomes.

58 een tumor and bone marrow glucose uptake and poor outcomes.

59 ) is an uncommon hematologic malignancy with poor outcomes.

60 which is often undiagnosed and can result in poor outcomes.

61 er B7-H4, signatures of fibrotic stroma, and poor outcomes.

62 dmission digoxin therapy was associated with poor outcomes.

63 stem, may be accentuated in HIV and leads to poor outcomes.

64 rs," delayed ICU transfer is associated with poor outcomes.

65 de glioma (HGG), a subgroup with universally poor outcomes.

66 ng patients and identify the risk factors of poor outcomes.

67 ism active in high-grade gliomas that drives poor outcomes.

68 apies for Wilms tumor (WT) patients who have poor outcomes.

69 the pancreas is a major problem resulting in poor outcomes.

70 s with infection who are at elevated risk of poor outcomes.

71 markers could improve risk stratification of poor outcomes.

72 bjects have more rapid cognitive decline and poor outcomes.

73 calcitrant to treatment, and associated with poor outcomes.

74 abolism but largely lack treatments and have poor outcomes.

75 loid leukemia (AML) and a FLT3 mutation have poor outcomes.

76 acute kidney injury (AKI) is associated with poor outcomes.

77 zinamide concentrations were associated with poor outcomes.

78 dren with moderate to severe latent RHD have poor outcomes.

79 with high titers, which are associated with poor outcomes.

80 utations were more frequent in patients with poor outcomes.

81 ine and ART were identified as high-risk for poor outcomes.

82 been associated with delayed treatments and poor outcomes.

83 ith cancer metastasis, tumor recurrence, and poor outcomes.

84 roup of early stage patients associated with poor outcomes.

85 ecting systemic inflammation associated with poor outcomes.

86 up and/or adjuvant therapy to mitigate their poor outcomes.

87 easing length of stay may be associated with poor outcomes.

88 psis, or apnea were clinical determinants of poor outcomes.

89 t patients, approximately 1 in 3 still had a poor outcome 1 year after TAVR.

90 ibited cICA-PO and more likely to experience poor outcomes (80.0% vs. 25.8%, P < 0.001), hemorrhagic

91 -risk category with increased likelihood of "poor outcomes:" a greater than 10% chance of dying or an

92 sease, since high HAT1 levels associate with poor outcomes across multiple cancer types.

93 EEG allows for reliable prediction of poor outcome after cardiac arrest, with maximum sensitiv

94 d that ECMO is an independent risk factor of poor outcome after dMCS.

95 nd/or BCL6 rearrangements (HGBL-DH/TH) has a poor outcome after standard chemoimmunotherapy.

96 ytomegalovirus (CMV) remains associated with poor outcomes after kidney transplantation (kTx).

97 an identify patients at an increased risk of poor outcomes after liver transplantation (LT) based onl

98 otein (AFP) > 1,000 ng/mL is associated with poor outcomes after liver transplantation (LT) for hepat

99 nts with cICA-POs are more likely to exhibit poor outcomes after MT, particularly when recanalization

100 ted long-term overall survival or those with poor outcomes after receiving conventional treatments.

101 demonstrates that APOE4 is a risk factor for poor outcomes after rmTBI and highlights how personalize

102 arge B-cell lymphoma (DLBCL) associated with poor outcomes after standard chemoimmunotherapy.

103 kely to be discarded because of concerns for poor outcomes after transplantation.

104 geted temperature management toward good and poor outcomes, along with other recognized predictors.

105 Serious fall injuries are associated with poor outcomes among dialysis patients, but whether these

106 hether underlying asthma was associated with poor outcomes among hospitalized patients with severe CO

107 use of different therapeutic foods has shown poor outcomes among supplemented malnourished children.

108 nifested as a comatose state, is a marker of poor outcome and a major basis for unfavorable neurologi

109 n of ARF in the nucleolus is associated with poor outcome and attenuated response to chemotherapy.

110 This study analysed predictors of poor outcome and their prognostic value after an ICA.

111 Most of the poor outcomes and deaths of cardiac arrest survivors hav

112 mor microenvironment and have been linked to poor outcomes and drug resistance.

113 ctal adenocarcinomas (PDAC), and drive their poor outcomes and failure to respond to targeted therapi

114 or autologous stem-cell transplantation have poor outcomes and few treatment options.

115 urgitation (FTR) due to the awareness of its poor outcomes and potential percutaneous therapies.

116 ve phenotype that may be a primary driver of poor outcomes and submit that immunomodulatory therapeut

117 ation between infants destined to experience poor outcomes and those not; comparing median Apgars bet

118 ding understanding of the risks that lead to poor outcomes and which protective factors contribute to

119 genomic alteration as a potent predictor of poor outcome, and is a community resource for further in

120 g these diagnoses, may help stratify risk of poor outcomes, and provide opportunities for more focuse

121 Several variables predicted poor outcomes, and regular use of beta-blockers correlat

122 fear of penalties to transplant centers for poor outcomes, and stigma surrounding the quality of lif

123 s the basis for care in early psychosis, and poor outcomes are common.

124 Genetic determinants for poor outcomes are unknown.

125 detect any combinations of events predicting poor outcome as defined by a cumulative CCI >=37.1 at 90

126 week 1 of S100B and NSE were associated with poor outcome, as were highest concentration overall and

127 m 34 younger women, 17 with good and 17 with poor outcomes, as determined by disease-specific surviva

128 o failed recanalization (n = 15) experienced poor outcomes, as did 69.2% of patients in whom recanali

129 Whether poor outcome associated with cannabis use is mediated th

130 Variable standards of care may contribute to poor outcomes associated with AKI.

131 to adequate prenatal care may contribute to poor outcomes associated with preeclampsia in African Am

132 SCLC) is a highly aggressive malignancy with poor outcomes associated with resistance to cisplatin-ba

133 al pathways and mechanisms that culminate in poor outcomes associated with sarcopenia.

134 ction and its treatment were associated with poor outcomes at month 3 follow-up among children encoun

135 ted clinical net benefit in minimizing false poor outcome attribution might potentially prevent unwar

136 ilities (0.8, aiming to avoid false-positive poor outcome attribution), that the max-ICH Score provid

137 IR) had favorable performance for predicting poor outcome (AUC > 0.750), and had better results than

138 howed favorable performance for predicting a poor outcome (AUC > 0.750), and were better than the rad

139 e same factors and Asian ethnicity predicted poor outcome, but persistent smoking had the greatest im

140 in hospitalized children is associated with poor outcomes, but no contemporary study has reported wh

141 ibroblasts (CAFs) are known to contribute to poor outcome by conferring therapy resistance to various

142 ia, as well as the variables associated with poor outcomes, can yield insight into potential interven

143 Predictors of poor outcome collected on hospital admission may inform

144 gher monocyte counts were at higher risk for poor outcomes (COMET Wilcoxon p=0.025; Yale Wilcoxon p=0

145 riables tested were strongly associated with poor outcome (CSF culture positivity, CSF white blood ce

146 proportion of participants with a composite poor outcome (defined as viral load >50 copies per mL, o

147 as a rapidly progressive clinical course and poor outcome due to its refractoriness to conventional c

148 cancer (TNBC) is associated with relatively poor outcomes due to its metastatic propensity, frequent

149 s an independent risk factor associated with poor outcome during the early phase after dMCS (hazard r

150 development is associated with high risk of poor outcome even after adjustment for underlying injury

151 between reduced brain tissue oxygenation and poor outcome following severe traumatic brain injury has

152 nth retention rates seldom exceeding 50% and poor outcomes following dropout, we must explore innovat

153 increased susceptibility to infections, and poor outcomes following injury.

154 ment because of potential fetal harm risks a poor outcome for both mother and child.

155 ese findings challenge the current notion of poor outcome for CRC with immediate or early metastases.

156 ssociated macrophages (TAMs) correlates with poor outcome for many tumors, so to determine if there w

157 one will learn about the concerns regarding poor outcome for men who receive female donor hearts and

158 nt from chromosomal amplification and drives poor outcome for patients across many cancer types.

159 lling association between NIX expression and poor outcome for patients with glioblastoma.

160 SS3/mesotrypsin expression is prognostic for poor outcome for patients with lung adenocarcinoma, and

161 Poor outcomes for hospitalized PLWH were frequent but si

162 ed with cellular heterogeneity contribute to poor outcomes for MITF-low melanoma patients and that MI

163 This study highlights poor outcomes for patients with BPDCN in the modern era

164 a transcriptional signature is predictive of poor outcomes for patients, but little is known about it

165 to identify patients who are most at risk of poor outcomes from cancer treatment and to better alloca

166 curity, major diet-related comorbidities for poor outcomes from COVID-19 such as diabetes, hypertensi

167 rculating tumor cells (CTCs) correlated with poor outcomes from the use of abiraterone and enzalutami

168 We hypothesized that poor outcomes from ZIKV infection during pregnancy are d

169 658, respectively) and identified those with poor outcomes (GOS-E, </=4 vs >4) (AUC = 0.771 and 0.777

170 the presence of a number of risk factors for poor outcomes, had a relatively mild clinical course.

171 stic left heart syndrome (HLHS) with risk of poor outcome has been linked to MYH6 variants, implicati

172 One reason for this poor outcome has been that no treatment programme has ev

173 Such poor outcomes have fuelled ongoing efforts to exploit th

174 I:1.14-3.82, p = 0.017) were associated with poor outcome if not in baseline but present in final vis

175 EEG-R was considered reliable for predicting poor outcome if specificity was >=95%.

176 A distinct poor-outcome immunomodulatory microenvironment, hitherto

177 associated with chemotherapy resistance and poor outcome in acute myeloid leukemia (AML).

178 dicted by the profiles, were associated with poor outcome in both patient cohorts.

179 ging druggable vulnerabilities predictive of poor outcome in BRAF(V600E) patients.

180 antly associated with distant metastasis and poor outcome in breast cancer patients.

181 y, and mutational signatures associated with poor outcome in early lung ADC, with potential future im

182 The 17q23 amplicon is associated with poor outcome in ER(+) breast cancers, but the causal gen

183 ber of aberration calls) was associated with poor outcome in FL.

184 onomous PADI4 inhibition was associated with poor outcome in human breast cancer datasets, consistent

185 olume, and electrographic seizures predicted poor outcome in lobar intraparenchymal hemorrhage.

186 n ER(+ve)/luminal tumors was associated with poor outcome in luminal B cancers.

187 ion is associated with treatment failure and poor outcome in metastatic castration-resistant prostate

188 that span the TP53 gene are associated with poor outcome in multiple myeloma (MM), but the prognosti

189 ly, high NOVA2 expression is associated with poor outcome in ovarian cancer patients.

190 seline serum IL-8 levels are associated with poor outcome in patients (n = 1,344) with advanced cance

191 K2 (but not PRK1) expression correlates with poor outcome in patients with basal-like/Triple Negative

192 d an association between PKCe expression and poor outcome in patients with lung adenocarcinoma specif

193 lagens enriched in the tumor coinciding with poor outcome in patients with ovarian cancer.

194 CD84 expression levels were associated with poor outcome in patients with stroke.

195 is correlates with lymph node metastasis and poor outcome in several human malignancies.

196 r for angiogenesis, has been associated with poor outcome in several types of cancer.

197 ion of a threat and might be associated with poor outcome in the critically ill.

198 is fully integrated model was a predictor of poor outcome in the independent cohort (concordance, 0.7

199 nt of genomic imbalances are associated with poor outcome in younger breast cancer patients and thus

200 of patients, with excellent, good, fair, and poor outcomes in 45.6%, 44.3%, 6.2%, and 3.9% of cases,

201 sation registry to identify risk factors for poor outcomes in adult patients with community-acquired

202 Metabolic acidosis is associated with poor outcomes in CKD.

203 and glycemic variability are associated with poor outcomes in critically ill patients.

204 The poor outcomes in esophageal adenocarcinoma (EAC) prompte

205 itive for identifying the biology underlying poor outcomes in GCB-DLBCL.

206 erebral perfusion pressure may contribute to poor outcomes in hypertensive intraventricular hemorrhag

207 Poor outcomes in individuals with rising eGFR are potent

208 The poor outcomes in infant acute lymphoblastic leukemia (AL

209 High expression of CCAR2 correlates with poor outcomes in many human tumor types such as squamous

210 POE4 and APOE2 variants confer favorable and poor outcomes in melanoma, respectively.

211 Poor outcomes in new-onset status epilepticus were assoc

212 tion by a single species, has been linked to poor outcomes in patients undergoing hematopoietic cell

213 ne use of corticosteroids is associated with poor outcomes in patients with non-small-cell lung cance

214 are but aggressive non-Hodgkin lymphoma with poor outcomes in patients with relapsed/refractory (R/R)

215 y promote the basal type of PDAC, conferring poor outcomes in patients.

216 statements offer insight into predictors of poor outcomes in pediatric CD and are valuable when deve

217 imary objective was to determine the risk of poor outcomes in relation to bacteremia duration.

218 -induced coagulopathy is common and portends poor outcomes in severely-injured children.

219 on with BI strains of C. difficile predicted poor outcomes in the MODIFY I/II trials.

220 ich might lead to systematic underdosing and poor outcomes in these children.

221 upport (ECLS) is challenging due to expected poor outcomes in these patients.

222 LT3 internal tandem duplications (ITDs) have poor outcomes, in particular AML with a high (>=0.5) mut

223 Predictors of poor outcome included age between 28 days and 1 year (co

224 in Asia is increasing and is associated with poor outcomes including hepatocellular carcinoma and dea

225 dney injury is common and is associated with poor outcomes, including increased mortality, among crit

226 howed that the signature was associated with poor outcome independently of well-defined prognostic fa

227 ant to avoid pursuing futile treatments when poor outcome is inevitable but also to avoid an inapprop

228 l mitochondrial function are associated with poor outcomes like fetal growth restriction.

229 keratitis who are at risk of experiencing a poor outcome may be useful to allocate resources toward

230 erstanding severity in anaphylaxis, in which poor outcomes may occur as a result of a failure in comp

231 vance support of patients and families after poor outcomes, may improve clinical care and reduce clai

232 biological mechanisms linking lower SES and poor outcomes; much of this work has examined the relati

233 One of these areas is the prediction of poor outcome, notably radiographic outcome in patients w

234 underlie the increased cancer incidence and poor outcomes observed in African American patients.

235 Poor outcome occurred in 86 patients at 30 days.

236 city was still independently associated with poor outcome (odds ratio, 3.32; p = 0.003).

237 cICA-PO was independently associated with a poor outcome (odds ratio, 4.278; 95% CI, 1.080-33.006; P

238 rrelated with more lymph node metastasis and poor outcome of GC patients.

239 ys in pancreatic tumor cells may improve the poor outcome of pancreatic ductal adenocarcinoma (PDA).

240 evels in NSCLC patients, and correlates with poor outcome of patients with lung adenocarcinoma.

241 ociated with a more aggressive phenotype and poor outcome of patients, although more specific signatu

242 Rationale: Poor outcomes of adults surviving critical illness are w

243 brutinib resistance will greatly improve the poor outcomes of ibrutinib-exposed MCL patients.

244 heterogeneity, which has contributed to the poor outcomes of numerous clinical trials and continues

245 dence of post-operative trichiasis and other poor outcomes of trichiasis surgery in Africa.

246 ely), and were independently associated with poor outcome on multivariable analysis.

247 d-stage renal disease and is associated with poor outcomes on dialysis.

248 Odds ratio (OR) for predicting poor outcome or standardized mean difference (SMD) of th

249 y grades (2 and 3) were also associated with poor outcomes OR 9.541; 95% CI: 2.94-30.91; P = .001).

250 a-fetoprotein and c-MET were associated with poor outcome (overall survival) independently of regoraf

251 tratified patients into high and low risk of poor outcome (P < .001).

252 r hemodynamic severe sMR was associated with poor outcome (p = 0.017).

253 , with even higher dp-ucMGP in patients with poor outcomes (p<0.001).

254 eprocedural renal disease is associated with poor outcomes, particularly in stage 4 or 5 renal diseas

255 I findings at onset were more likely to have poor outcome (Pediatric Cerebral Performance Category sc

256 ith the ICH Score (14.1 vs 2.1 net predicted poor outcomes per 100 patients).

257 Within patients with poor outcome, PISA positively correlated with IL-6 (r =

258 Absence of EEG-R for poor outcome prediction had a specificity of 82% and a s

259 Specificity for poor outcome prediction increased from 98% to 99% when E

260 For poor outcome prediction, it has no substantial added val

261 Fifty patients with unexplained fever and poor outcomes presented at Irrua Specialist Teaching Hos

262 Baseline predictors of poor outcomes provide more accurate assessment of the po

263 on medicine program to benefit children with poor-outcome, rare, relapsed or refractory cancer.

264 So, certain patients with ACLF-3 have poor outcomes regardless of MELD-Na score.

265 e at particularly high risk of infection and poor outcomes related to coronavirus disease 2019 (COVID

266 Although poor outcomes reported from clinical practice are multif

267 sarcopenia and muscle wasting relate to such poor outcomes requires looking beyond the overt muscle l

268 aphic seizures, hyperexcitable patterns, and poor outcomes (score of 1-2 on Glasgow Outcome Scale) in

269 ons found impetus for improvement because of poor outcome scores or because they desired early career

270 ession and a signature of REDD1 loss predict poor outcomes selectively in RAS mutant but not RAS wild

271 Modifiable risk factors associated with poor outcomes should prompt evidence-based interventions

272 ical risk factors and are more predictive of poor outcomes than the rate of development of hyponatrem

273 terleukin-8 (pIL-8) has been associated with poor outcome to immune checkpoint blockade (1), this has

274 FLT3 internal tandem duplication (ITD) have poor outcomes to current treatment.

275 ed for tier assignment, which ranges from 0 (poor outcomes) to 1 (good outcomes), with eventual 1-yea

276 At 36 hours or later, sensitivity for poor outcome was <=0.22.

277 Poor outcome was defined as invasive ventilation and/or

278 Prediction of poor outcome was most accurate at 12 hours, with a sensi

279 The risk for poor outcomes was higher in those with comorbidities and

280 as developed after variables associated with poor outcome were identified at multivariate analysis (K

281 pecificity and sensitivity for prediction of poor outcome were independent of age, sex, and initial r

282 Poor outcomes were associated with stellate interconnect

283 oups selected (eg, a subgroup of babies with poor outcomes were explicitly excluded), conference abst

284 common pathophysiologies contributing to the poor outcomes were impaired substrate delivery (n = 158,

285 urring mutations, high-risk presentation and poor outcomes were specific to multi-hit patients only.

286 osomal instability in CTC is associated with poor outcomes when detected in men with progressing mCRP

287 t may identify patients with IPF at risk for poor outcomes when exposed to immunosuppression.

288 sion B-cell acute lymphocytic leukaemia have poor outcomes when treated with regimens that do not con

289 epresent a high-risk patient population with poor outcomes when treated with upfront surgery alone.

290 0.000] as the most significant predictor of poor outcomes whereas MIV was not significant [OR, 0.99

291 itamin K insufficiency, which was related to poor outcome while hepatic procoagulant factor II remain

292 a group of patients with a very high risk of poor outcomes while awaiting LT.

293 ation of genomic alterations associated with poor outcome will allow earlier and better selection of

294 tment is poorly investigated but known for a poor outcome with high rates of re-evisceration (redehis

295 ALF patients have poor outcomes with 30-day mortality of 26.7% and high ec

296 High risk subtypes continue to have poor outcomes with event free survival rates <40% despit

297 ma, an aggressive malignancy associated with poor outcomes with recurrent disease.

298 us patterns with >=50% suppression predicted poor outcome without false positives at >=6 hours after

299 t in schizophrenia is a major contributor to poor outcomes, yet its causes are poorly understood.

300 sis of tuberculous meningitis (TBM) leads to poor outcomes, yet the current diagnostic methods for id