Abstract
Background and purpose
The aim of this study was to determine whether early and late death are associated with different baseline factors in intracerebral haemorrhage (ICH) survivors.
Methods
This was a secondary analysis of the multicentre prospective observational CROMIS‐2 ICH study. Death was defined as ‘early’ if occurring within 6 months of study entry and ‘late’ if occurring after this time point.
Results
In our cohort (n = 1094), there were 306 deaths (per 100 patient‐years: absolute event rate, 11.7; 95% confidence intervals, 10.5–13.1); 156 were ‘early’ and 150 ‘late’. In multivariable analyses, early death was independently associated with age [per year increase; hazard ratio (HR), 1.05, P = 0.003], history of hypertension (HR, 1.89, P = 0.038), pre‐event modified Rankin scale score (per point increase; HR, 1.41, P < 0.0001), admission National Institutes of Health Stroke Scale score (per point increase; HR, 1.11, P < 0.0001) and haemorrhage volume >60 mL (HR, 4.08, P < 0.0001). Late death showed independent associations with age (per year increase; HR, 1.04, P = 0.003), pre‐event modified Rankin scale score (per point increase; HR, 1.42, P = 0.001), prior anticoagulant use (HR, 2.13, P = 0.028) and the presence of intraventricular extension (HR, 1.73, P = 0.033) in multivariable analyses. In further analyses where time was treated as continuous (rather than dichotomized), the HR of previous cerebral ischaemic events increased with time, whereas HRs for Glasgow Coma Scale score, National Institutes of Health Stroke Scale score and ICH volume decreased over time.
Conclusions
We provide new evidence that not all baseline factors associated with early mortality after ICH are associated with mortality after 6 months and that the effects of baseline variables change over time. Our findings could help design better prognostic scores for later death after ICH.
Keywords: intracerebral haemorrhage, mortality, prognosis
Introduction
Most research on outcomes following intracerebral haemorrhage (ICH) has focussed on short‐term mortality (within 6 months), reflecting the high rates of early death associated with this stroke subtype [1, 2]. Many factors associated with early mortality relate to ICH severity and this is reflected in prognostic scores that aim to predict outcome in the short term [3, 4, 5, 6, 7, 8, 9, 10, 11–3, 4, 5, 6, 7, 8, 9, 10, 11]. Policies of active acute management, including blood pressure lowering, prompt reversal of anticoagulation and neurosurgical referral, aim to improve prognosis in patients with ICH [12]. A better understanding of the factors that influence ‘late’ death following ICH might identify potentially modifiable risk factors that could improve long‐term outcomes [1].
Our aim was to evaluate whether early and late death are associated with different baseline factors in ICH survivors using data from the prospective multicentre CROMIS‐2 ICH study. We hypothesized that factors relating to the severity of the acute ICH would not be associated with death at later (beyond 6 months) time points.
Methods
Data availability statement
Analyses for the CROMIS‐2 study are ongoing; once all of these analyses are completed, the CROMIS‐2 Steering Committee will consider applications from other researchers for access to anonymized source data.
Participants
We included adults from the CROMIS‐2 (Clinical Relevance of Microbleeds in Stroke) ICH study; full details of the study protocol have been published previously [13]. Further details are provided in the Supporting Information. The study was approved by the National Research Ethics Service (IRAS reference 10/H0716/61). Informed written consent was obtained for all participants.
Outcomes
The outcome of interest for this project was time to death. Mortality notifications were received from NHS Digital (previously the Health and Social Care Information Centre) as detailed in the previously published study protocol [13]. NHS Digital is a national centralized body that collects data on health and social care in the UK; mortality data are derived from ‘hospital episode statistics’ (records of all NHS patient admissions) and information on registered deaths from the Office of National Statistics (death registration is a legal requirement in the UK).
Patients were censored at either 3 years following the ICH that resulted in study entry or at last available follow‐up for vital status (the time of the study’s last notification of deaths from NHS Digital, i.e. 31 October 2017), depending on which was earlier.
Imaging
Brain computed tomography imaging was acquired acutely at the time of the index event as part of the patient’s routine clinical care. Further details are provided in the Supporting Information.
Statistics
Statistical analysis was performed using Stata (version 15.1, StataCorp LLC, College Station, TX, USA). We dichotomized time following ICH into ‘early’ (before 6 months) and ‘late’ (after 6 months) periods. We used univariable Cox regression to calculate hazard ratios (HRs) for all baseline variables collected to review for associations during these two time periods. Variables where the 95% confidence intervals (CI) did not cross 1 were considered as statistically significant. To explore this further, the effect of each baseline variable was then allowed to vary linearly with time; further details are provided in the Supporting Information.
Results
All 1094 patients recruited to CROMIS‐2 ICH were included (Table 1). Follow‐up was for a total of 2613.48 patient‐years (median 3.00 years; interquartile range, 2.31–3.00 years). There were 306 deaths (absolute event rate, 11.7 per 100 patient‐years; 95% CI, 10.5–13.1 per 100 patient‐years; Fig. 1). The median time between the index ICH event and study entry was 4 days (interquartile range, 2–8 days).
Table 1.
Baseline characteristics
| All | Alive | Early death (<6 months) | Late death (≥6 months) | |
|---|---|---|---|---|
| n | 1094 | 788 (72.0%) | 156 (14.3%) | 150 (13.7%) |
| Age (years) | 73.3 ± 12.5 | 70.3 ± 12.4 | 81.1 ± 9.4 | 80.7 ± 8.5 |
| Sex, male | 628 (57.4%) | 468 (59.4%) | 78 (50.0%) | 82 (54.7%) |
| Hypertension | 718 (66.7%) | 505 (65.3%) | 114 (73.6%) | 99 (66.9%) |
| Hypercholesterolaemia | 467 (44.0%) | 322 (42.0%) | 71 (47.7%) | 74 (50.3%) |
| Diabetes mellitus | 202 (18.6%) | 132 (16.9%) | 38 (24.4%) | 32 (21.6%) |
| Atrial fibrillation | 375 (37.4%) | 215 (30.1%) | 81 (56.3%) | 79 (55.2%) |
| Smoking (at time of ICH) | 114 (10.8%) | 94 (12.4%) | 12 (8.0%) | 8 (5.6%) |
| Pre‐existing cognitive impairment | 251 (39.8%) | 150 (34.3%) | 54 (50.5%) | 47 (54.7%) |
| Previous cerebral ischaemic event | 241 (22.9%) | 149 (19.5%) | 44 (30.1%) | 48 (33.8%) |
| Previous ICH | 46 (4.3%) | 28 (3.6%) | 10 (6.7%) | 8 (5.6%) |
| Pre‐event mRS score | 0 (0–1) | 0 (0–1) | 1 (0–3) | 1 (0–2) |
| APOE ε2, presence | 189 (20.7%) | 138 (20.8%) | 31 (26.5%) | 20 (15.3%) |
| APOE ε4, presence | 256 (28.1%) | 196 (29.5%) | 24 (20.5%) | 36 (27.5%) |
| Medications | ||||
| Antiplatelet use prior to ICH | 267 (24.6%) | 193 (24.7%) | 38 (24.5%) | 36 (24.2%) |
| Anticoagulant use prior to ICH | 436 (40.1%) | 261 (33.4%) | 86 (55.5%) | 89 (59.3%) |
| Antiplatelet use at discharge | 65 (6.4%) | 46 (6.2%) | 8 (6.2%) | 11 (7.8%) |
| Anticoagulant use at discharge | 113 (10.7%) | 78 (10.2%) | 14 (9.5%) | 21 (14.5%) |
| Clinical features at study entry | ||||
| GCS score | 15 (14–15) | 15 (14–15) | 14 (11–15) | 15 (14–15) |
| NIHSS score | 7 (3–13) | 6 (3–11) | 14 (7–19) | 6 (3–12) |
| Imaging features at study entry | ||||
| Lacunes, presence | 98 (9.0%) | 69 (8.8%) | 15 (9.6%) | 14 (9.3%) |
| Van Swieten score (WMC) | 0 (0–2) | 0 (0–2) | 1 (0–3) | 2 (0–3) |
| ICH location | ||||
| Infratentorial | 99 (9.1) | 69 (8.8) | 12 (7.7) | 18 (12.0) |
| Deep | 546 (50.0) | 398 (50.6) | 69 (44.2) | 79 (52.7) |
| Lobar | 447 (40.9) | 319 (40.6) | 75 (48.1) | 53 (35.3) |
| ICH volume | ||||
| <30 mL | 886 (85.9) | 655 (89.0) | 106 (70.7) | 125 (85.6) |
| 30–60 mL | 99 (9.6) | 60 (8.2) | 24 (16.0) | 15 (10.3) |
| >60 mL | 47 (4.6) | 21 (2.9) | 20 (13.3) | 6 (4.1) |
| Intraventricular extension | 301 (27.7) | 183 (23.4) | 68 (43.6) | 50 (33.6) |
Percentage values were calculated using the total number of patients for whom data were available as the denominator. Data are given as mean ± SD, n (%) and median (interquartile range). GCS, Glasgow Coma Scale; ICH, intracerebral haemorrhage; IQR, interquartile range; mRS, modified Rankin scale; NIHSS, National Institutes of Health Stroke Scale; WMC, white matter changes.
Figure 1.
Unadjusted cumulative mortality curve.
Associations of ‘early’ versus ‘late’ death
Of the 306 deaths, 156 occurred within 6 months of the index haemorrhage event (‘early’) and 150 deaths occurred after 6 months and within 3 years of the index ICH (‘late’). Baseline characteristics for both groups are shown in Table 1.
Early death (Table 2) was associated with age, hypertension, diabetes mellitus, atrial fibrillation, a history of previous cerebral ischaemic events, pre‐event modified Rankin scale (mRS) score, anticoagulant use prior to ICH, Glasgow Coma Scale (GCS) and National Institutes of Health Stroke Scale (NIHSS) scores at study entry. Imaging features at study entry that were significantly associated with early death were Van Swieten score, ICH volume and the presence of intraventricular extension. In a multivariable model including these variables, age at study entry (per year increase; HR, 1.05; 95% CI, 1.02–1.08, P = 0.003), history of hypertension (HR, 1.89; 95% CI, 1.04–3.46, P = 0.038), pre‐event mRS score (per point increase; HR, 1.41; 95% CI, 1.17–1.70, P < 0.0001), admission NIHSS score (per point increase; HR, 1.11; 95% CI, 1.06–1.15, P < 0.0001) and ICH volume >60 mL (HR, 4.08; 95% CI, 1.85–8.96, P < 0.0001) remained associated with early death.
Table 2.
Univariable Cox regression analysis for early (before 6 months) and late (after 6 months) periods following intracerebral haemorrhage (ICH)
| ‘Early’ | ‘Late’ | Time‐varying coefficient P‐value | |
|---|---|---|---|
| Age (per year increase) | 1.08 (1.06–1.10) | 1.09 (1.07–1.11) | 0.360 |
| Sex, male | 0.73 (0.53–1.00) | 0.85 (0.62–1.18) | 0.500 |
| Hypertension | 1.43 (1.00–2.04) | 1.08 (0.77–1.52) | 0.270 |
| Hypercholesterolaemia | 1.18 (0.86–1.63) | 1.34 (0.97–1.85) | 0.585 |
| Diabetes mellitus | 1.44 (1.00–2.07) | 1.31 (0.89–1.94) | 0.729 |
| Atrial fibrillation | 2.31 (1.66–3.21) | 2.67 (1.92–3.71) | 0.548 |
| Smoking, current | 0.70 (0.39–1.27) | 0.45 (0.22–0.91) | 0.337 |
| Pre‐existing cognitive impairment | 1.32 (0.75–2.34) | 2.13 (1.39–3.25) | 0.336 |
| Previous cerebral ischaemic event | 1.50 (1.05–2.13) | 1.90 (1.34–2.69) | 0.345 |
| Previous ICH | 1.65 (0.87–3.14) | 1.49 (0.73–3.04) | 0.833 |
| Pre‐event mRS score (per point increase) | 1.56 (1.40–1.74) | 1.50 (1.33–1.69) | 0.610 |
| APOE ε2, presence | 1.40 (0.93–2.11) | 0.70 (0.44–1.13) | 0.032 |
| APOE ε4, presence | 0.65 (0.42–1.02) | 0.90 (0.61–1.32) | 0.280 |
| Medications | |||
| Antiplatelet use prior to ICH | 0.98 (0.68–1.42) | 0.96 (0.66–1.40) | 0.936 |
| Anticoagulant use prior to ICH | 1.97 (1.44–2.71) | 2.73 (1.97–3.78) | 0.164 |
| Antiplatelet use at discharge | 0.95 (0.46–1.93) | 1.23 (0.67–2.28) | 0.582 |
| Anticoagulant use at discharge | 0.85 (0.49–1.48) | 1.48 (0.93–2.35) | 0.134 |
| Clinical features at study entry | |||
| GCS score (per point increase) | 0.80 (0.76–0.84) | 0.93 (0.86–1.00) | 0.001 |
| NIHSS score (per point increase) | 1.11 (1.08–1.14) | 1.00 (0.97–1.04) | <0.0001 |
| Imaging features at study entry | |||
| Lacunes, presence | 1.05 (0.62–1.79) | 1.06 (0.61–1.84) | 0.976 |
| Van Swieten score (WMC, per point increase) | 1.23 (1.11–1.36) | 1.37 (1.23–1.51) | 0.139 |
| ICH location | |||
| Infratentorial | Reference group | ||
| Deep | 1.04 (0.57–1.93) | 0.79 (0.47–1.32) | 0.494 |
| Lobar | 1.42 (0.77–2.62) | 0.67 (0.39–1.14) | 0.067 |
| ICH volume | |||
| <30 mL | Reference group | ||
| 30–60 mL | 2.20 (1.41–3.42) | 1.29 (0.75–2.20) | 0.131 |
| >60 mL | 4.85 (3.01–7.83) | 1.40 (0.62–3.18) | 0.010 |
| Intraventricular extension | 2.20 (1.61–3.02) | 1.55 (1.11–2.18) | 0.141 |
Univariable hazard ratios (HRs) for each characteristic obtained by fitting Cox regression models with time‐varying effects (before/after 6 months). Data are given as HR (95% confidence intervals). The time‐varying coefficient P‐value compares the difference between the early and late HRs. GCS, Glasgow Coma Scale; mRS, modified Rankin scale; NIHSS, National Institutes of Health Stroke Scale.
When considering death later than 6 months (Table 2), age, atrial fibrillation, smoking, pre‐event cognitive impairment, previous cerebral ischaemic event, pre‐event mRS score, anticoagulant use prior to index ICH, increasing van Swieten score and the presence of intraventricular extension showed significant associations. In a multivariable model including all variables with a significant association with late death, only age at study entry (per year increase; HR, 1.04; 95% CI, 1.02–1.08, P = 0.003), pre‐event mRS score (per point increase; HR, 1.42; 95% CI, 1.16–1.73, P = 0.001), anticoagulant use prior to ICH (HR, 2.13; 95% CI, 1.08–4.17, P = 0.028) and the presence of intraventricular extension (HR, 1.73; 95% CI, 1.05–2.85, P = 0.033) remained associated with late death.
We then investigated which baseline characteristics showed a significant change in HR between the early and late periods (Table 2). We found that HRs for the presence of APOE ε2, GCS score, NIHSS score and ICH volume >60 mL showed evidence of significant change between the early and late periods (Table 2).
Further exploratory analysis of time‐varying effects
We then performed exploratory analyses where time was considered as a continuous measure (Table S1). In these analyses, variables that showed significant time‐varying effects were history of a previous cerebral ischaemic event (P = 0.0214), admission GCS score (P = 0.0108), NIHSS score (P < 0.00001) and ICH volume (P = 0.0439). The HRs of previous cerebral ischaemic events increased with time, whereas those for NIHSS score and ICH volume decreased with time. The protective (negative association) of GCS score also decreased with time.
Discussion
We provide new evidence that not all baseline factors associated with early mortality after ICH are associated with mortality after 6 months. In analyses where the time‐varying effects of baseline variables were allowed to vary continuously with time, we found that the influence of measures of acute ICH severity decreased over time, whereas those associated with established cerebrovascular disease (previous cerebral ischaemic events) increased over time. These results support the argument that definitions of ‘early’ or ‘late’ death are necessarily arbitrary, as the impact of some characteristics present at study entry vary continuously with time.
In our study, the factors that we found to be independently associated with early death are in keeping with other studies and reflected in pre‐existing prognostic scores that include these and other variables [4, 5, 6, 7, 8, 9, 10, 11]. Differences between our results for associations with late death and those previously reported [14, 15] are likely to reflect our method of considering early and late death independently; when considering all death events together, we observed effects that were similar to those previously reported. Additionally, we observed that four variables (APOE ε2, GCS score, NIHSS score and ICH volume >60 mL) showed significant differences in the magnitude of their effect before and after 6 months (although the HRs for APOE ε2 were not statistically significant in themselves). This result confirms that, whereas GCS score, NIHSS score and ICH volume are important predictors of early mortality, their effect changes significantly between the early and late periods, and thus they are less useful for predicting mortality in the longer term, as we hypothesized. Our analyses of linear time‐varying effects on long‐term mortality following ICH are novel and demonstrate the potentially complex interactions that can occur over time. These analyses highlight the difficulties in defining what is ‘early’ death or a ‘short‐term’ outcome; further work that considers time‐varying effects on mortality across longer time scales is needed to guide this.
Our finding of an association between intraventricular extension and late death seems counterintuitive, but illustrates the importance of our work and the complicated manner in which baseline variables might interact over time. Given that our cohort included patients with milder strokes, we hypothesize that the effect of intraventricular extension on early death was lost in the adjusted analyses because of larger magnitude effects associated with other factors associated with ICH severity (NIHSS score and ICH volume). We speculate that, when considering late death, the effects of acute factors such as NIHSS score and ICH volume were of smaller magnitude, and thus the impact of intraventricular extension as a measure of stroke severity became more apparent. We did observe, in unadjusted analyses, that intraventricular extension was associated with both early and late death, but the magnitude of the association was smaller for late death (Table 2).
Our study has a number of strengths, including the number of patients, its multicentre design (which increases generalizability), robust ascertainment of follow‐up events and the detailed clinical and radiological data available for each participant. However, there are limitations of our work. Our cohort is comprised of survivors with mild strokes, as reflected by the median NIHSS and GCS scores, low ICH volumes and low early death rates. This cohort is therefore unlikely to be representative of all patients with ICH, particularly those with more severe haemorrhages. We were unable to adjust for acute complications of ICH or details relating to immediate care, either active or care‐limiting (i.e. do not resuscitate orders or palliative pathways), all of which would impact mortality. Additionally, we were unable to comment on cause of death in our patients. Finally, although we considered the time‐varying effects of variables recorded at study entry, the status of these may have changed after this time point (e.g. antiplatelet or anticoagulant use) and this could have influenced our results.
We provide new evidence that not all baseline factors associated with early mortality after ICH are associated with mortality after 6 months. Our findings could help design better prognostic scores for later death after ICH.
Disclosure of conflicts of interest
H.C. reports grants and other support from Bayer Healthcare and UCB outside the submitted work. T.Y. reports personal fees and other support from GlaxoSmithKline, Biogen Idec, Novartis, ESOR, Merck, Hikma and Parexel outside the submitted work. G.Y.H.L. reports consultancy and speaker fees from Bayer, Bayer/Janssen, BMS/Pfizer, Biotronik, Medtronic, Boehringer Ingelheim, Microlife, Roche and Daiichi‐Sankyo outside the submitted work; no fees are directly received personally. K.W.M. reports personal fees from Bayer, personal fees and non‐financial support from Boehringer Ingelheim and personal fees from Daiichi‐Sankyo outside the submitted work. D.J.W. reports personal fees from Bayer, Alnylam and Portola outside the submitted work. The remaining authors declare no financial or other conflicts of interest.
Supporting information
Table S1. Hazard ratios for variables with a significant time‐varying effect, at time 0 (study entry) and then 1, 2 and 3 years subsequently.
Acknowledgements
The CROMIS‐2 study is funded by the Stroke Association and British Heart Foundation. G.B. holds a National Institute for Health Research (NIHR) Academic Clinical Fellowship and received funding from the Rosetrees Trust. G.A. receives funding from the NIHR UCLH Biomedical Research Centres (BRC). T.Y. is supported by grants from the Medical Research Council, MS Society of Great Britain and Northern Ireland, Stroke Association, British Heart Foundation and Wellcome Trust. M.M.B.’s Chair in Stroke Medicine is supported by the Reta Lila Weston Trust for Medical Research. D.J.W. receives support from the Stroke Association, British Heart Foundation and Rosetrees Trust. This work was undertaken at UCLH/UCL, which receives a proportion of funding from the Department of Health’s NIHR BRC funding scheme.
Appendix 1.
The CROMIS‐2 collaborators
Louise Shaw MD, Kirsty Harkness MD, Jane Sword MD, Azlisham Mohd Nor MD, Pankaj Sharma PhD, Deborah Kelly MD, Frances Harrington MD, Marc Randall MD, Matthew Smith MD, Karim Mahawish MD, Abduelbaset Elmarim MD, Bernard Esisi MD, Claire Cullen MD, Arumug Nallasivam MD, Christopher Price MD, Adrian Barry MD, Christine Roffe MD, John Coyle MD, Ahamad Hassan MD, Caroline Lovelock DPhil, Jonathan Birns MD, David Cohen MD, L. Sekaran MD, Adrian Parry‐Jones PhD, Anthea Parry MD, David Hargroves MD, Harald Proschel MD, Prabel Datta MD, Khaled Darawil MD, Aravindakshan Manoj MD, Mathew Burn MD, Chris Patterson MD, Elio Giallombardo MD, Nigel Smyth MD, Syed Mansoor MD, Ijaz Anwar MD, Rachel Marsh MD, Sissi Ispoglou MD, Dinesh Chadha MD, Mathuri Prabhakaran MD, Sanjeevikumar Meenakishundaram MD, Janice O'Connell MD, Jon Scott MD, Vinodh Krishnamurthy MD, Prasanna Aghoram MD, Michael McCormick MD, Paul O’Mahony MD, Martin Cooper MD, Lillian Choy MD, Peter Wilkinson MD, Simon Leach MD, Sarah Caine MD, Ilse Burger MD, Gunaratam Gunathilagan MD, Paul Guyler MD, Hedley Emsley MD, Michelle Davis MD, Dulka Manawadu MD, Kath Pasco MD, Maam Mamun MD, Robert Luder MD, Mahmud Sajid MD, Ijaz Anwar MD, James Okwera MD, Julie Staals PhD, Elizabeth Warburton MD, Kari Saastamoinen MD, Timothy England MD, Janet Putterill MD, Enrico Flossman MD, Michael Power MD, Krishna Dani MD, David Mangion MD, Appu Suman MD, John Corrigan MD, Enas Lawrence MD and Djamil Vahidassr MD.
Contributor Information
G. Banerjee, Email: g.banerjee@ucl.ac.uk.
the CROMIS‐2 collaborators:
Louise Shaw, Kirsty Harkness, Jane Sword, Azlisham Mohd Nor, Pankaj Sharma, Deborah Kelly, Frances Harrington, Marc Randall, Matthew Smith, Karim Mahawish, Abduelbaset Elmarim, Bernard Esisi, Claire Cullen, Arumug Nallasivam, Christopher Price, Adrian Barry, Christine Roffe, John Coyle, Ahamad Hassan, Caroline Lovelock, Jonathan Birns, David Cohen, L. Sekaran, Adrian Parry‐Jones, Anthea Parry, David Hargroves, Harald Proschel, Prabel Datta, Khaled Darawil, Aravindakshan Manoj, Mathew Burn, Chris Patterson, Elio Giallombardo, Nigel Smyth, Syed Mansoor, Ijaz Anwar, Rachel Marsh, Sissi Ispoglou, Dinesh Chadha, Mathuri Prabhakaran, Sanjeevikumar Meenakishundaram, Janice O'Connell, Jon Scott, Vinodh Krishnamurthy, Prasanna Aghoram, Michael McCormick, Paul O’Mahony, Martin Cooper, Lillian Choy, Peter Wilkinson, Simon Leach, Sarah Caine, Ilse Burger, Gunaratam Gunathilagan, Paul Guyler, Hedley Emsley, Michelle Davis, Dulka Manawadu, Kath Pasco, Maam Mamun, Robert Luder, Mahmud Sajid, Ijaz Anwar, James Okwera, Julie Staals, Elizabeth Warburton, Kari Saastamoinen, Timothy England, Janet Putterill, Enrico Flossman, Michael Power, Krishna Dani, David Mangion, Appu Suman, John Corrigan, Enas Lawrence, and Djamil Vahidassr
References
- 1. Moulin S, Cordonnier C. Prognosis and outcome of intracerebral haemorrhage. Front Neurol Neurosci 2015; 37: 182–192. [DOI] [PubMed] [Google Scholar]
- 2. Poon MT, Fonville AF, Al‐Shahi Salman R. Long‐term prognosis after intracerebral haemorrhage: systematic review and meta‐analysis. J Neurol Neurosurg Psychiatry 2014; 85: 660–667. [DOI] [PubMed] [Google Scholar]
- 3. Specogna AV, Turin TC, Patten SB, Hill MD. Factors associated with early deterioration after spontaneous intracerebral hemorrhage: a systematic review and meta‐analysis. PLoS ONE 2014; 9: e96743. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Rost NS, Smith EE, Chang Y, et al Prediction of functional outcome in patients with primary intracerebral hemorrhage: the FUNC score. Stroke 2008; 39: 2304–2309. [DOI] [PubMed] [Google Scholar]
- 5. Hemphill JC 3rd, Bonovich DC, Besmertis L, Manley GT, Johnston SC. The ICH score: a simple, reliable grading scale for intracerebral hemorrhage. Stroke 2001; 32: 891–897. [DOI] [PubMed] [Google Scholar]
- 6. Cheung RT, Zou LY. Use of the original, modified, or new intracerebral hemorrhage score to predict mortality and morbidity after intracerebral hemorrhage. Stroke 2003; 34: 1717–1722. [DOI] [PubMed] [Google Scholar]
- 7. Godoy DA, Pinero G, Di Napoli M. Predicting mortality in spontaneous intracerebral hemorrhage: can modification to original score improve the prediction? Stroke 2006; 37: 1038–1044. [DOI] [PubMed] [Google Scholar]
- 8. Weimar C, Roth M, Willig V, Kostopoulos P, Benemann J, Diener HC. Development and validation of a prognostic model to predict recovery following intracerebral hemorrhage. J Neurol 2006; 253: 788–793. [DOI] [PubMed] [Google Scholar]
- 9. Ruiz‐Sandoval JL, Chiquete E, Romero‐Vargas S, Padilla‐Martinez JJ, Gonzalez‐Cornejo S. Grading scale for prediction of outcome in primary intracerebral hemorrhages. Stroke 2007; 38: 1641–1644. [DOI] [PubMed] [Google Scholar]
- 10. Cho DY, Chen CC, Lee WY, Lee HC, Ho LH. A new Modified Intracerebral Hemorrhage score for treatment decisions in basal ganglia hemorrhage – a randomized trial. Crit Care Med 2008; 36(7): 2151–2156. [DOI] [PubMed] [Google Scholar]
- 11. Chuang YC, Chen YM, Peng SK, Peng SY. Risk stratification for predicting 30‐day mortality of intracerebral hemorrhage. Int J Qual Health Care 2009; 21: 441–447. [DOI] [PubMed] [Google Scholar]
- 12. Paroutoglou K, Parry‐Jones AR. Hyperacute management of intracerebral haemorrhage. Clin Med (Lond) 2018; 18(Suppl 2): s9–s12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Charidimou A, Wilson D, Shakeshaft C, et al The Clinical Relevance of Microbleeds in Stroke study (CROMIS‐2): rationale, design, and methods. Int J Stroke 2015; 10(SA100): 155–161. [DOI] [PubMed] [Google Scholar]
- 14. Hansen BM, Nilsson OG, Anderson H, Norrving B, Saveland H, Lindgren A. Long term (13 years) prognosis after primary intracerebral haemorrhage: a prospective population based study of long term mortality, prognostic factors and causes of death. J Neurol Neurosurg Psychiatry 2013; 84: 1150–1155. [DOI] [PubMed] [Google Scholar]
- 15. Tveiten A, Ljostad U, Mygland A, Naess H. Leukoaraiosis is associated with short‐ and long‐term mortality in patients with intracerebral hemorrhage. J Stroke Cerebrovasc Dis 2013; 22: 919–925. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1. Hazard ratios for variables with a significant time‐varying effect, at time 0 (study entry) and then 1, 2 and 3 years subsequently.
Data Availability Statement
Analyses for the CROMIS‐2 study are ongoing; once all of these analyses are completed, the CROMIS‐2 Steering Committee will consider applications from other researchers for access to anonymized source data.