Arsalan Ahmad

Predictors of In-Hospital Mortality after Decompressive Hemicraniectomy for Malignant Ischemic Stroke

OBJECTIVEnThe purpose of this retrospective multicenter, pooled-data analysis was to determine the factors associated with in-hospital mortality in decompressive hemicraniectomy (DHC) for malignant middle cerebral artery (MMCA) stroke.nnnPATIENTS AND METHODSnThe authors reviewed pooled DHC database from 3 countries for patients with MMCA with hospital mortality in spite of DHC to identify factors that predicted in-hospital mortality after DHC. The identified factors were applied to the group of patients who were selected for DHC but either refused surgery and died or stabilized and did not undergo DHC.nnnFINDINGSnThere were 137 patients who underwent DHC. Multiple logistic regression analysis showed middle cerebral artery (MCA) with additional infarcts (odds ratio [OR], 7.9: 95% confidence interval [CI], 2.4-26; Pu2009=u2009.001), preoperative midline shift of septum pellucidumu2009of 1u2009cm or more (OR, 3.83: 95% CI, 1.13-12.96; Pu2009=u2009.031), and patients who remained unconscious on day 7 postoperatively (8.82: 95% CI; OR, 1.08-71.9; Pu2009=u2009.042) were significant independent predictors for in-hospital mortality. The identified factors were applied to the group of MMCA patients not operated (nu2009=u200919 refused, nu2009=u200947 stabilized) single (Pu2009<u2009.001), and two predictive factors (Pu2009<u2009.001) were significantly more common in patients who died. Whereas two predicative factors were identified in only 9%-18.2% of survivors, the presence of all three predictive factors was seen only in patients who expired (Pu2009<u2009.001). The Hosmer-Lemeshow goodness-of-fit statistics (chi-squareu2009=u20094.65; P valueu2009=u2009.589) indicate that the model adequately describes the data.nnnCONCLUSIONnDirect physical factors, such as MCA with additional territory infarct, extent of midline shift, and postoperative consciousness level, bore a significant relationship to in-hospital mortality in MMCA patients undergoing DHC.

Economic perspective of dementia care in Pakistan

Dementia has a range of outcomes that includes declining memory, multiple cognitive deficits, and mortality due to brain damage. Developing countries bear the burden of 60% of patients with dementia worldwide.1 Pakistan is the sixth most populated country and has an estimated 150,000–200,000 patients with dementia. In light of improving health indicators, average life expectancy has increased. With an increase in the elderly population, the prevalence of dementia is expected to rise. Among people older than 65 years, the prevalence of dementia is expected to increase from 2% to 6% over the next 30 years.2 This poses an economic challenge for the population that bears 67% of the cost of health care through out-of-pocket payments.3 Traditionally most health care is consumed by the population in the latter age spectrum; therefore people in Pakistan who retire at the age of 60 rely on the government pension scheme and their savings for health care costs.

Prediction of infarction volume and infarction growth rate in acute ischemic stroke

The prediction of infarction volume after stroke onset depends on the shape of the growth dynamics of the infarction. To understand growth patterns that predict lesion volume changes, we studied currently available models described in literature and compared the models with Adaptive Neuro-Fuzzy Inference System [ANFIS], a method previously unused in the prediction of infarction growth and infarction volume (IV). We included 67 patients with malignant middle cerebral artery [MMCA] stroke who underwent decompressive hemicraniectomy. All patients had at least three cranial CT scans prior to the surgery. The rate of growth and volume of infarction measured on the third CT was predicted with ANFIS without statistically significant difference compared to the ground truth [Pu2009=u20090.489]. This was not possible with linear, logarithmic or exponential methods. ANFIS was able to predict infarction volume [IV3] over a wide range of volume [163.7–600u2009cm3] and time [22–110u2009hours]. The cross correlation [CRR] indicated similarity between the ANFIS-predicted IV3 and original data of 82% for ANFIS, followed by logarithmic 70%, exponential 63% and linear 48% respectively. Our study shows that ANFIS is superior to previously defined methods in the prediction of infarction growth rate (IGR) with reasonable accuracy, over wide time and volume range.