Lumbar decompression surgery for spinal canal stenosis outcome

Lumbar decompression surgery for spinal canal stenosis outcome

Lumbar laminectomy, represents the standard operative treatment for lumbar spinal stenosis, but this procedure is often combined with fusion surgery. It is still discussed whether minimal-invasive decompression procedures are sufficient and if they compromise spinal stability as well.

Decompression of lumbar spinal stenosis without fusion led to a significant and similar reduction of back pain and leg pain in a short-term and a long-term follow-up group. Patients without previous surgery benefited significantly better, whereas patients with previous decompression benefited regarding back pain, especially for long-term follow-up with a clear trend in favor of leg pain 1).

Currently, there is interest in minimally invasive surgery and various technical modifications of decompressive lumbar laminectomy without fusion.

Particularly, depression has been shown to be associated with less improvement following lumbar fusion surgery 2) 3) 4) 5) 6) 7) 8).

Karp et al. 9) reviewed 158 patients who underwent epidural spinal injections for low-back pain with or without radiculopathy. These investigators found that depression and sleep disturbance were prognostic of worse Patient-Reported Outcome Measurement Information System (PROMIS) outcomes following epidural spinal injections.

Hägg et al. 10) performed a randomized controlled trial of 264 patients with severe chronic low-back pain who underwent either surgical or nonsurgical treatment, and assessed the impact of underlying affective disorders. They found that baseline depression correlated with worse outcomes following both operative and nonoperative treatment.

Interestingly, they also observed that depressed patients tended to have better outcomes with nonoperativecare, whereas nondepressed patients tended to have better outcomes with fusion.

In the study of Lubelski et al. 11) found that worsening depression (as measured by the PHQ-9) independently significantly predicted worse EQ-5D index outcomes following conservative treatment for LSS (p = 0.0002). This effect was most evident when comparing patients with severe depression, who improve 0.14 points less than those with no depression. This difference exceeds the MCID and confirms that depression is a poor prognostic factor for QOL improvement following nonoperative treatment for LSS. Further investigation is needed to determine whether treatment of depression prior to conservative or surgical management of LSS will improve posttreatment QOL outcomes. There are several limitations that should be considered when interpreting the results. Multiple treating physicians were included, and factors such as participation in physical therapy, treatment with NSAIDs, opioid medications and other nonsurgical treatments varied by practitioner and patient; this increases the variability, but also improves the generalizability.

They adjusted for the increased variability by using the random effect in the regression models. Many patients were also lost to follow-up at the 4-month evaluation.

The cohorts were similar for most characteristics; however, there were statistically significant, albeit small differences for estimated percent below poverty threshold and median income by zip code. The analysis is only valid for patients who did follow-up assessments at these time points. Additionally, this was a retrospective study with a relatively short follow-up period.

Prospectively designed studies with longer follow-up are needed to further validate the findings. Nonetheless, this is the largest study investigating the correlation between depression and QOL outcomes following conservative management of LSS.

Lubelski et al. have used the validated PHQ-9 measure of depression and have found a statistically and clinically significant impact on EQ-5D index outcomes.

The results of this study suggest that depressed patients with LSS have significantly less improvement following conservative management compared with nondepressed patients. Both physicians and surgeons who treat patients with LSS should consider using validated questionnaires such as the PHQ-9 for pretreatment evaluation of depression, to better assess the likelihood of success following treatment. Further investigation is needed to evaluate the effect of depression treatment prior to management of the spinal disorder. Future prospective studies with longer follow-up intervals may be useful in further evaluating the QOL outcomes in this patient population 12).


In cases of lumbar spinal stenosis (LSS) treated with surgical decompression, a postoperative magnetic resonance imaging (MRI) is sometimes required. In the experience of a study, the obtained decompression observed on early postoperative MRI tends to be disappointing compared to the decompression achieved intraoperatively. This raises the question of whether the early postoperative MRI, performed after lumbar decompression, is a fair representation of the ‘real’ decompression. A study investigated the correlation between intraoperative and postoperative measurements of the lumbar spinal canal.

Surgical decompression of the spinal canal effectively decreases the compression of the dural sac. However, early postoperative MRI after lumbar decompression does not adequately represent the decompression achieved intraoperatively 13).

Back pain improvement

Through the 1st postoperative year, patients with lumbar stenosis-without spondylolisthesis, scoliosis, or sagittal malalignment-and clinically significant back pain improved after decompression-only surgery 14).


The most common surgical method currently used is lumbar laminectomy, with complete decompression; this technique has a 5-year follow-up effective rate of 81.6% 15).

Apart from acute complications such as hematoma and infections, same-level recurrent lumbar stenosis and adjacent-segment disease (ASD) are factors that can occur after index lumbar spine surgery.

While looking for predictors of revision surgery due to re-stenosis, instability or same/adjacent segment disease none of these were found. Within our cohort no significant differences concerning demographic, peri-operative and radiographic data of patients with or without revision wer noted. Patients, who needed revision surgery were older but slightly healthier while more likely to be male and smoking. Surprisingly, significant differences were noted regarding the distribution of intraoperative and early postoperative complications among the 6 main surgeons while these weren’t obious within the intial index group of late revisions 16).


A systematic review was conducted using MEDLINE for literature published through December 2014. The first question focused on the effectiveness of lumbar spine surgery for symptomatic lumbar spinal stenosis in elderly patients. The second question focused on safety of surgical intervention on this elderly population with emphasis on perioperative complication rates.

Review of 11 studies reveals that the majority of elderly patients exhibit significant symptomatic improvement, with overall benefits observed for pain (change visual analog scale4.4 points) and disability (change Oswestry Disability Index 23 points). Review of 11 studies reveals that perioperative complications were infrequent and acceptable with pooled estimates of mortality (0.5%), inadvertent durotomy (5%), and wound infection (2%). Outcomes seem less favorable with greater complication rates among patients with diabetesor obesity.

Based on largely low-quality, retrospective evidence, Shamji et al. recommend that elderly patients should not be excluded from surgical intervention for symptomatic lumbar spinal stenosis 17).

Fusion Is Not a Safeguard to Prevent Revision Surgery in Lumbar Spinal Stenosis 18).

A cohort study showed no significant association between the type of index operation for Degenerative Lumbar Spinal Stenosis-decompression alone or fusion-and the need for revision surgery or the outcomes of pain, disability, and quality of life among patients after 3 years. Number of revision operations was associated with more pain and worse quality of life 19).


1)

Geiger MF, Bongartz N, Blume C, Clusmann H, Müller CA. Improvement of Back and Leg Pain after Lumbar Spinal Decompression without Fusion. J Neurol Surg A Cent Eur Neurosurg. 2018 Dec 5. doi: 10.1055/s-0038-1669473. [Epub ahead of print] PubMed PMID: 30517963.
2)

Aalto TJ, Malmivaara A, Kovacs F, Herno A, Alen M, Salmi L, et al: Preoperative predictors for postoperative clinical outcome in lumbar spinal stenosis: systematic review. Spine (Phila Pa 1976) 31:E648–E663, 2006
3)

Adogwa O, Parker SL, Shau DN, Mendenhall SK, Aaronson OS, Cheng JS, et al: Preoperative Zung Depression Scale predicts outcome after revision lumbar surgery for adjacent segment disease, recurrent stenosis, and pseudarthrosis. Spine J 12:179–185, 2012
4)

Adogwa O, Parker SL, Shau DN, Mendenhall SK, Bydon A, Cheng JS, et al: Preoperative Zung depression scale predicts patient satisfaction independent of the extent of improvement after revision lumbar surgery. Spine J 13:501–506, 2013
5)

Arpino L, Iavarone A, Parlato C, Moraci A: Prognostic role of depression after lumbar disc surgery. Neurol Sci 25:145– 147, 2004
6)

Chaichana KL, Mukherjee D, Adogwa O, Cheng JS, McGirt MJ: Correlation of preoperative depression and somatic perception scales with postoperative disability and quality of life after lumbar discectomy. J Neurosurg Spine 14:261– 267, 2011
7)

Sinikallio S, Aalto T, Airaksinen O, Herno A, Kröger H, Viinamäki H: Depressive burden in the preoperative and early recovery phase predicts poorer surgery outcome among lumbar spinal stenosis patients: a one-year prospective follow-up study. Spine (Phila Pa 1976) 34:2573–2578, 2009
8)

Trief PM, Grant W, Fredrickson B: A prospective study of psychological predictors of lumbar surgery outcome. Spine (Phila Pa 1976) 25:2616–2621, 2000
9)

Karp JF, Yu L, Friedly J, Amtmann D, Pilkonis PA: Negative affect and sleep disturbance may be associated with response to epidural steroid injections for spine-related pain. Arch Phys Med Rehabil 95:309–315, 2014
10)

Hägg O, Fritzell P, Ekselius L, Nordwall A: Predictors of outcome in fusion surgery for chronic low back pain. A report from the Swedish Lumbar Spine Study. Eur Spine J 12:22–33, 2003
11) , 12)

Lubelski D, Thompson NR, Bansal S, Mroz TE, Mazanec DJ, Benzel EC, Khalaf T. Depression as a predictor of worse quality of life outcomes following nonoperative treatment for lumbar stenosis. J Neurosurg Spine. 2015 Mar;22(3):267-72. doi: 10.3171/2014.10.SPINE14220. Epub 2014 Dec 19. PubMed PMID: 25525957.
13)

Schenck C, van Susante J, van Gorp M, Belder R, Vleggeert-Lankamp C. Lumbar spinal canal dimensions measured intraoperatively after decompression are not properly rendered on early postoperative MRI. Acta Neurochir (Wien). 2016 May;158(5):981-8. doi: 10.1007/s00701-016-2777-5. Epub 2016 Mar 23. PubMed PMID: 27005673; PubMed Central PMCID: PMC4826663.
14)

Crawford CH 3rd, Glassman SD, Mummaneni PV, Knightly JJ, Asher AL. Back pain improvement after decompression without fusion or stabilization in patients with lumbar spinal stenosis and clinically significant preoperative back pain. J Neurosurg Spine. 2016 Nov;25(5):596-601. PubMed PMID: 27285666.
15)

Bouras T, Stranjalis G, Loufardaki M, Sourtzis I, Stavrinou LC, Sakas DE. Predictors of long-term outcome in an elderly group after laminectomy for lumbar stenosis. J Neurosurg Spine. 2010;59:329–34.
16)

Melcher C, Paulus AC, Roßbach BP, Gülecyüz MF, Birkenmaier C, Schulze-Pellengahr CV, Teske W, Wegener B. Lumbar spinal stenosis – surgical outcome and the odds of revision-surgery: Is it all due to the surgeon? Technol Health Care. 2022 Jun 10. doi: 10.3233/THC-223389. Epub ahead of print. PMID: 35754243.
17)

Shamji MF, Mroz T, Hsu W, Chutkan N. Management of Degenerative Lumbar Spinal Stenosis in the Elderly. Neurosurgery. 2015 Oct;77 Suppl 4:S68-74. doi: 10.1227/NEU.0000000000000943. PubMed PMID: 26378360.
18)

Austevoll IM, Ebbs E. Fusion Is Not a Safeguard to Prevent Revision Surgery in Lumbar Spinal Stenosis. JAMA Netw Open. 2022 Jul 1;5(7):e2223812. doi: 10.1001/jamanetworkopen.2022.23812. PMID: 35881401.
19)

Ulrich NH, Burgstaller JM, Valeri F, Pichierri G, Betz M, Fekete TF, Wertli MM, Porchet F, Steurer J, Farshad M; Lumbar Stenosis Outcome Study Group. Incidence of Revision Surgery After Decompression With vs Without Fusion Among Patients With Degenerative Lumbar Spinal Stenosis. JAMA Netw Open. 2022 Jul 1;5(7):e2223803. doi: 10.1001/jamanetworkopen.2022.23803. PMID: 35881393.

Severe traumatic brain injury outcome

Severe traumatic brain injury outcome

Females exhibited more favorable cerebral physiology post-Traumatic Brain Injury, particularly better mitochondrial function, and reduced excitotoxicity, but this did not translate into better clinical outcomes compared to males. Future studies need to further explore potential sex differences in secondary injury mechanisms in TBI 1).


deep learning model of head computed tomography and clinical information can be used to predict 6-month severe traumatic brain injury outcome 2).


Younger age, modified Fisher scale (mFS) score, and Intracerebral hemorrhage volume are associated with Intracranial pressure elevation in patients with a severe traumatic brain injury. Imaging features may stratify patients by their risk of subsequent ICP elevation 3).


There has been a secular trend towards reduced incidence of severe traumatic brain injury in the first world, driven by public health interventions such as seatbelt legislation, helmet use, and workplace health and safety regulations. This has paralleled improved outcomes following TBI delivered in a large part by the widespread establishment of specialised neurointensive care 4).

The impact of a moderate to severe brain injury depends on the following:

Severity of initial injury

Rate/completeness of physiological recovery

Functions affected

Meaning of dysfunction to the individual

Resources available to aid recovery

Areas of function not affected by TBI

see Effect of trauma center designation in severe traumatic brain injury outcome


Mortality or severe disability affects the majority of patients after severe traumatic brain injury (TBI). Adherence to the brain trauma foundation severe traumatic brain injury guidelines has overall improved outcomes; however, traditional as well as novel interventions towards intracranial hypertension and secondary brain injury have come under scrutiny after series of negative randomized controlled trials. In fact, it would not be unfair to say there has been no single major breakthrough in the management of severe TBI in the last two decades. One plausible hypothesis for the aforementioned failures is that by the time treatment is initiated for neuroprotection, or physiologic optimization, irreversible brain injury has already set in. Lazaridis et al., and others, have developed predictive models based on machine learning from continuous time series of intracranial pressure and partial pressure of brain tissue oxygen. These models provide accurate predictions of physiologic crises events in a timely fashion, offering the opportunity for an earlier application of targeted interventions. In a article, Lazaridis et al., review the rationale for prediction, discuss available predictive models with examples, and offer suggestions for their future prospective testing in conjunction with preventive clinical algorithm5).


Determining the prognostic significance of clinical factors for patients with severe head injury can lead to an improved understanding of the pathophysiology of head injury and to improvement in therapy. A technique known as the sequential Bayes method has been used previously for the purpose of prognosis. The application of this method assumes that prognostic factors are statistically independent. It is now known that they are not. Violation of the assumption of independence may produce errors in determining prognosis. As an alternative technique for predicting the outcome of patients with severe head injury, a logistic regression model is proposed. A preliminary evaluation of the method using data from 115 patients with head injury shows the feasibility of using early data to predict outcome accurately and of being able to rank input variables in order of their prognostc significance. 6).


A prospective and consecutive series of 225 patients with severe head injuries who were managed in a uniform way was analyzed to relate outcome to several clinical variables. Good recovery or moderate disability were achieved by 56% of the patients, 10% remained severely disabled or vegetative, and 34% died. Factors important in predicting a poor outcome included the presence of intracranial hematoma, increasing agemotor impairment, impaired or absent eye movements or pupillary light reflexes, early hypotensionhypoxemia or hypercarbia, and raised intracranial pressure over 20 mm Hg despite artificial ventilation. Most of these predictive factors were assessed on admission, but a subset of 158 patients was identified in whom coma was present on admission and was known to have persisted at least until the following day. Although the mortality in this subset (40%) was higher than in the total series, it was lower than in several comparable reported series of patients with severe head injury. Predictive correlations were equally strong in the entire series and in the subset of 158 patients with coma. A plea is made for inclusion in the definition of “severe head injury” of all patients who do not obey commands or utter recognizable words on admission to the hospital after early resuscitation 7).


Survival rate of isolated severe TBI patients who required an emergent neurosurgical intervention could be time dependent. These patients might benefit from expedited process (computed tomographic scan, neurosurgical consultation, etc.) to shorten the time to surgical intervention 8).

The impact of a moderate to severe brain injury can include:

Cognitive deficits including difficulties with:

Attention Concentration Distractibility Memory Speed of Processing Confusion Perseveration Impulsiveness Language Processing “Executive functions” Speech and Language

not understanding the spoken word (receptive aphasia) difficulty speaking and being understood (expressive aphasia) slurred speech speaking very fast or very slow problems reading problems writing Sensory

difficulties with interpretation of touch, temperature, movement, limb position and fine discrimination Perceptual

the integration or patterning of sensory impressions into psychologically meaningful data Vision

partial or total loss of vision weakness of eye muscles and double vision (diplopia) blurred vision problems judging distance involuntary eye movements (nystagmus) intolerance of light (photophobia) Hearing

decrease or loss of hearing ringing in the ears (tinnitus) increased sensitivity to sounds Smell

loss or diminished sense of smell (anosmia) Taste

loss or diminished sense of taste Seizures

the convulsions associated with epilepsy that can be several types and can involve disruption in consciousness, sensory perception, or motor movements Physical Changes

Physical paralysis/spasticity Chronic pain Control of bowel and bladder Sleep disorders Loss of stamina Appetite changes Regulation of body temperature Menstrual difficulties Social-Emotional

Dependent behaviors Emotional ability Lack of motivation Irritability Aggression Depression Disinhibition Denial/lack of awareness


Both single predictors from early clinical examination and multiple hospitalization variables/parameters can be used to determine the long-term prognosis of TBI. Predictive models like the IMPACT or CRASH prognosis calculator (based on large sample sizes) can predict mortality and unfavorable outcomes. Moreover, imaging techniques like MRI (Magnetic Resonance Imaging) can also predict consciousness recovery and mental recovery in severe TBI, while biomarkers associated with stress correlate with, and hence can be used to predict, severity and mortality. All predictors have limitations in clinical application. Further studies comparing different predictors and models are required to resolve limitations of current predictors 9).

Clinical outcome prediction following traumatic brain injury (TBI) is a widely investigated field of research. Several outcome prediction models have been developed for prognosis after TBI. There are two main prognostic models: International Mission for Prognosis and Clinical Trials in Traumatic Brain Injury (IMPACT) prognosis calculator and the Corticosteroid Randomization after Significant Head Injury (CRASH) prognosis calculator. The prognosis model has three or four levels:

(1) model A included age, motor GCS, and pupil reactivity

(2) model B included predictors from model A with CT characteristics

(3) model C included predictors from model B with laboratory parameters.

In consideration of the fact that interventions after admission, such as ICP management also have prognostic value for outcome predictions and may improve the models’ performance, Yuan F et al developed another prediction model (model D) which includes ICP. With the development of molecular biology, a handful of brain injury biomarkers were reported that may improve the predictive power of prognostic models, including neuron-specific enolase (NSE), glial fibrillary acid protein (GFAP), S-100β protein, tumour necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), myelin basic protein (MBP), cleaved tau protein (C-tau), spectrin breakdown products (SBDPs), and ubiquitin C-terminal hydrolase-L1 (UCH-L1), and sex hormones. A total of 40 manuscripts reporting 11 biomarkers were identified in the literature. Many substances have been implicated as potential biomarkers for TBI; however, no single biomarker has shown the necessary sensitivity and specificity for predicting outcome. The limited number of publications in this field underscores the need for further investigation. Through fluid biomarker analysis, the advent of multi-analyte profiling technology has enabled substantial advances in the diagnosis and treatment of a variety of conditions. Application of this technology to create a bio-signature for TBI using multiple biomarkers in combination will hopefully facilitate much-needed advances. We believe that further investigations about brain injury biomarkers may improve the predictive power of the contemporary outcome calculators and prognostic models, and eventually improve the care of patients with TBI 10).


Injury site, injury type, and injury degree are the main risk factors for post-traumatic epilepsyTraumatic brain injury outcome can be affected by early post-traumatic epilepsy11).

Insurance and racial disparities continue to exist for TBI patients. Insurance status appears to have an impact on short- and long-term outcomes to a greater degree than patient race 12).

CRASH

IMPACT

Traumatic brain injury mortality.

see Quality of Life after Brain Injury.

Traumatic brain injury complications.

Statins have been shown to improve traumatic brain injury outcome in animal models. The aim of a study was to determine the effect of preinjury statins on outcomes in TBI patients.

Lokhandwala et al. performed a 4-y (2014-2017) review of a TBI database and included all patients aged ≥18 y with severe isolated TBI. Patients were stratified into those who were on statins and those who were not and were matched (1:2 ratio) using propensity score matching. The primary outcome was in-hospital mortality. The secondary outcomes were skilled nursing facility disposition, Glasgow Outcome Scale-extended score, and hospital and intensive care unit length of stay (LOS).

They identified 1359 patients, of which 270 were matched (statin: 90, no-statin: 180). Mean age was 55 ± 8y, median Glasgow Coma Scale was 10 (8-12), and median head-abbreviated injury scale was 3 (3-5). Matched groups were similar in age, mechanism of injury, Glasgow Coma Scale, Injury Severity Score, neurosurgical intervention, type and size of intracranial hemorrhage, and preinjury anticoagulant or antiplatelet use. The overall in-hospital mortality rate was 18%. Patients who received statins had lower rates of in-hospital mortality (11% versus 21%, P = 0.01), skilled nursing facility disposition (19% versus 28%; P = 0.04), and a higher median Glasgow Outcome Scale-extended (11 [9-13] versus 9 [8-10]; P = 0.04). No differences were found between the two groups in terms of hospital LOS (6 [4-9] versus 5 [3-8]; P = 0.34) and intensive care unit LOS (3 [3-6] versus 4 [3-5]; P = 0.09).

Preinjury statin use in isolated traumatic brain injury patients is associated with improved outcomes. This finding warrants further investigations to evaluate the potential beneficial role of statins as a therapeutic drug in a TBI 13).


1)

Svedung Wettervik TM, Hånell A, Howells T, Enblad P, Lewén A. Females Exhibit Better Cerebral Pressure Autoregulation, Less Mitochondrial Dysfunction, and Reduced Excitotoxicity following Severe Traumatic Brain Injury. J Neurotrauma. 2022 May 19. doi: 10.1089/neu.2022.0097. Epub ahead of print. PMID: 35587145.
2)

Pease M, Arefan D, Barber J, Yuh E, Puccio A, Hochberger K, Nwachuku E, Roy S, Casillo S, Temkin N, Okonkwo DO, Wu S; TRACK-TBI Investigators. Outcome Prediction in Patients with Severe Traumatic Brain Injury Using Deep Learning from Head CT Scans. Radiology. 2022 Apr 26:212181. doi: 10.1148/radiol.212181. Epub ahead of print. PMID: 35471108.
3)

Murray NM, Wolman DN, Mlynash M, Threlkeld ZD, Christensen S, Heit JJ, Harris OA, Hirsch KG. Early Head Computed Tomography Abnormalities Associated with Elevated Intracranial Pressure in Severe Traumatic Brain Injury. J Neuroimaging. 2020 Nov 4. doi: 10.1111/jon.12799. Epub ahead of print. PMID: 33146933.
4)

Khellaf A, Khan DZ, Helmy A. Recent advances in traumatic brain injury. J Neurol. 2019 Sep 28. doi: 10.1007/s00415-019-09541-4. [Epub ahead of print] PubMed PMID: 31563989.
5)

Lazaridis C, Rusin CG, Robertson CS. Secondary Brain Injury: Predicting and Preventing Insults. Neuropharmacology. 2018 Jun 6. pii: S0028-3908(18)30279-X. doi: 10.1016/j.neuropharm.2018.06.005. [Epub ahead of print] Review. PubMed PMID: 29885419.
6)

Stablein DM, Miller JD, Choi SC, Becker DP. Statistical methods for determining prognosis in severe head injury. Neurosurgery. 1980 Mar;6(3):243-8. PubMed PMID: 6770283.
7)

Miller JD, Butterworth JF, Gudeman SK, Faulkner JE, Choi SC, Selhorst JB, Harbison JW, Lutz HA, Young HF, Becker DP. Further experience in the management of severe head injury. J Neurosurg. 1981 Mar;54(3):289-99. PubMed PMID: 7463128.
8)

Matsushima K, Inaba K, Siboni S, Skiada D, Strumwasser AM, Magee GA, Sung GY, Benjaminm ER, Lam L, Demetriades D. Emergent operation for isolated severe traumatic brain injury: Does time matter? J Trauma Acute Care Surg. 2015 Aug 28. [Epub ahead of print] PubMed PMID: 26317818.
9)

Gao L, Wu X. Prediction of clinical outcome in severe traumatic brain injury. Front Biosci (Landmark Ed). 2015 Jan 1;20:763-771. PubMed PMID: 25553477.
10)

Gao J, Zheng Z. Development of prognostic models for patients with traumatic brain injury: a systematic review. Int J Clin Exp Med. 2015 Nov 15;8(11):19881-5. eCollection 2015. Review. PubMed PMID: 26884899; PubMed Central PMCID: PMC4723744.
11)

Liu Z, Chen Q, Chen Z, Wang J, Tian D, Wang L, Liu B, Zhang S. Clinical analysis on risk factors and prognosis of early post-traumatic epilepsy. Arq Neuropsiquiatr. 2019 Jul 15;77(6):375-380. doi: 10.1590/0004-282×20190071. PubMed PMID: 31314838.
12)

Schiraldi M, Patil CG, Mukherjee D, Ugiliweneza B, Nuño M, Lad SP, Boakye M. Effect of Insurance and Racial Disparities on Outcomes in Traumatic Brain Injury. J Neurol Surg A Cent Eur Neurosurg. 2015 Mar 23. [Epub ahead of print] PubMed PMID: 25798799.
13)

Lokhandwala A, Hanna K, Gries L, Zeeshan M, Ditillo M, Tang A, Hamidi M, Joseph B. Preinjury Statins Are Associated With Improved Survival in Patients With Traumatic Brain Injury. J Surg Res. 2019 Aug 16;245:367-372. doi: 10.1016/j.jss.2019.07.081. [Epub ahead of print] PubMed PMID: 31425877.

Spontaneous intracerebral hemorrhage outcome

Spontaneous intracerebral hemorrhage outcome

ICH is a potentially devastating neurologic emergency with long-term functional independence achieved in only 12-39% of cases and mortality rates of 54% at 1 year 1).

Patients with spontaneous intracerebral hemorrhage have high mortality and poor outcome. It is the most serious, least treatable and more variable in incidence and management compared to other stroke subtypes 2) 3).

Although this is a heterogeneous disorder with a wide range of outcomes, overall mortality at 1 month is approximately 40%, and only 25% of patients have a favorable outcome 4) 5).

Case fatality is extremely high (reaching approximately 60 % at 1 year post event). Only 20 % of patients who survive are independent within 6 months 6).


Anion gap level is a potential predictive biomarker for the long-term outcomes of spontaneous intracerebral hemorrhage patients, and rectifying AG at admission improves the spontaneous intracerebral hemorrhage outcome7).


In a cohort (n=1094), there were 306 deaths (per 100 patient-years: absolute event rate 11.7, 95% CI 10.5 to 13.1); 156 were “early” and 150 “late”. In multivariable analyses, early death was independently associated with age (per year increase, HR 1.05, p=0.003), history of hypertension (HR 1.89, p=0.038), pre-event mRS (per point increase, HR 1.41, p<0.0001), admission NIHSS (per point increase, HR 1.11, p<0.0001), and hemorrhage volume > 60ml (HR 4.08, p<0.0001). Late death showed independent associations with age (per year increase, HR 1.04, p=0.003), pre-event mRS (per point increase, HR 1.42, p=0.001), prior anticoagulant use (HR 2.13, p=0.028) and the presence of intraventricular hemorrhage (HR 1.73, p=0.033) in multivariable analyses. In further analyses where time was treated as continuous (rather than dichotomized), the hazard ratio of previous cerebral ischaemic events increased with time, whilst those for GCS, NIHSS and ICH volume decreased over time.

They provided new evidence that not all baseline factors associated with early mortality after intracerebral hemorrhage are associated with mortality after 6 months, and that the effects of baseline variables change over time. The findings could help design better prognostic scores for later death after intracerebral hemorrhage 8).


As with other types of hemorrhages within the skull, intraparenchymal bleeds are a serious medical emergency because they can produce intracranial hypertension, which if left untreated can lead to coma and death.

Intracerebral hemorrhage (ICH) is a cerebrovascular disease with high mortality and morbidity, and the effective treatment is still lacking.

It is more likely to result in death or major disability than ischemic stroke or subarachnoid hemorrhage, and therefore constitutes an immediate medical emergency. Intracerebral hemorrhages and accompanying edema may disrupt or compress adjacent brain tissue, leading to neurological dysfunction. Substantial displacement of brain parenchyma may cause intracranial hypertension and potentially fatal brain herniation syndromes.

They have high rates of morbidity and rates of mortality of up to 50%. Initial hematoma size and subsequent hematoma expansion are among the most important predictors of poor outcome.

Efforts to improve clinical outcome through mitigation of hematoma expansion have so far been unsuccessful.

Data suggest that outcomes can be improved with standardized medical care.

A strong association exists between the amount of intraventricular hemorrhage (IVH) and poor outcome in intracerebral hemorrhage. An IVH volume of 5 to 10 mL emerges as a significant threshold for decision making on prognosis in these patients 9).

The ICH score is a simple and reliable clinical grading scale that is used for predicting the early mortality of patients with ICHs.

Neurological deterioration (ND) occurs frequently and predicts poor outcomes. Hematoma expansion and intraventricular hemorrhage in early ND, and cerebral edema, fever, and medical complications in later ND 10).


Hematoma expansion is a potentially modifiable predictor of poor outcome following an acute intracerebral hemorrhage (ICH). The ability to identify patients with ICH who are likeliest to experience hematoma expansion and therefore likeliest to benefit from expansion-targeted treatments remains an unmet need. Hypodensities within an ICH detected by noncontrast computed tomography (NCCT) have been suggested as a predictor of hematoma expansion.

There have been no dramatic advances in the development of interventions to improve the functional outcomes after ICH 11).

The purpose of a study was to establish and validate a nomogram to estimate the 30-day probability of death in patients with spontaneous intracerebral hemorrhage.. From January 2015 to December 2017, a cohort of 450 patients with clinically diagnosed cerebral hemorrhage was collected for model development. The minimum absolute contraction and the selection operator (lasso) regression model were used to select the strongest prediction of patients with cerebral hemorrhage. Discrimination and calibration were used to evaluate the performance of the resulting nomogram. After internal validation, the nomogram was further assessed in a different cohort containing 148 consecutive subjects examined between January 2018 and December 2018. The nomogram included five predictors from the lasso regression analysis, including Glasgow coma scale (GCS), hematoma locationhematoma volumewhite blood cells, and D-dimerInternal verification showed that the model had good discrimination, (the area under the curve is 0.955), and good calibration [unreliability (U) statistic, p = 0.739]. The nomogram still showed good discrimination (area under the curve = 0.888) and good calibration [U statistic, p = 0.926] in the verification cohort data. Decision curve analysis showed that the prediction nomogram was clinically useful. The current study delineates a predictive nomogram combining clinical and imaging features, which can help identify patients who may die of a cerebral hemorrhage 12).

The ICH Score is a valid clinical grading scale for long-term functional outcome after acute intracerebral hemorrhage (ICH) 13).

Based on the viewpoint that increased BP causes greater tearing of blood vessels and flow-out of blood through these vessels and eventually leads to the expansion of the hematoma, high BP is considered to be associated with hematoma expansion and poor outcomes, especially early neurological deterioration, mortality, and dependency 14) 15) 16).


The 2015 American Heart Association/American Stroke Association guidelines for the management of spontaneous ICH recommend early BP reduction with an SBP target of 140 mmHg for patients with ICH presenting with an SBP between 150 and 220 mmHg and without any contraindication to acute BP treatment 17).

Spontaneous intracerebral hemorrhage (SICH) survivors are at risk of hospital readmissions. Data on readmissions after SICH is scarce. We aimed to study the frequency and predictors of readmissions after SICH in Algarve, Portugal.

A retrospective study of a community representative cohort of SICH survivors (2009-2015). The first unplanned readmission in the first year after discharge was the outcome. Cox regression analysis was performed to identify predictors of 1-year readmission.

Of the 357 SICH survivors followed, 116 (32.5%) were readmitted within the first-year. Sixty-seven (18.8%) of the survivors were early readmitted (<90 days), corresponding to 57.8% or all readmissions. Common causes were pneumonia, endocrine/nutritional/metabolic and cardiovascular complications. The risk of readmission was increased by prior to index SICH history of ≥ 3 previous emergency department visits (hazards ratio (HR) = 2.663 (1.770-4.007); P < 0.001), pneumonia during index hospitalization (HR = 2.910 (1.844-4.592); P < 0.001) and reduced in patients discharge home (HR = 0.681 (0.366-0.976); P = 0.048).

The rate of readmissions after SICH is high, predictors are identifiable and causes are potentially preventable. Improvement of care can potentially reduce this burden 18).


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Han Q, Li M, Su D, Fu A, Li L, Chen T. Development and validation of a 30-day death nomogram in patients with spontaneous cerebral hemorrhage: a retrospective cohort study. Acta Neurol Belg. 2021 Feb 10. doi: 10.1007/s13760-021-01617-1. Epub ahead of print. PMID: 33566335.
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